Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2019

Lassnig Markus, Stabauer Petra, Breitfuß Gert, Müller Julian M.

Erfolgreiche Konzepte und Handlungsempfehlungen für digitale Geschäftsmodellinnovationen

HMD Edition, Springer Verlag, 2019

Buch
Zahlreiche Forschungsergebnisse im Bereich Geschäftsmodellinnovationen haben gezeigt, dass über 90 Prozent aller Geschäftsmodelle der letzten 50 Jahre aus einer Rekombination von bestehenden Konzepten entstanden sind. Grundsätzlich gilt das auch für digitale Geschäftsmodellinnovationen. Angesichts der Breite potenzieller digitaler Geschäftsmodellinnovationen wollten die Autoren wissen, welche Modellmuster in der wirtschaftlichen Praxis welche Bedeutung haben. Deshalb wurde die digitale Transformation mit neuen Geschäftsmodellen in einer empirischen Studie basierend auf qualitativen Interviews mit 68 Unternehmen untersucht. Dabei wurden sieben geeignete Geschäftsmodellmuster identifiziert, bezüglich ihres Disruptionspotenzials von evolutionär bis revolutionär klassifiziert und der Realisierungsgrad in den Unternehmen analysiert. Die stark komprimierte Conclusio lautet, dass das Thema Geschäftsmodellinnovationen durch Industrie 4.0 und digitale Transformation bei den Unternehmen angekommen ist. Es gibt jedoch sehr unterschiedliche Geschwindigkeiten in der Umsetzung und im Neuheitsgrad der Geschäftsmodellideen. Die schrittweise Weiterentwicklung von Geschäftsmodellen (evolutionär) wird von den meisten Unternehmen bevorzugt, da hier die grundsätzliche Art und Weise des Leistungsangebots bestehen bleibt. Im Gegensatz dazu gibt es aber auch Unternehmen, die bereits radikale Änderungen vornehmen, die die gesamte Geschäftslogik betreffen (revolutionäre Geschäftsmodellinnovationen). Entsprechend wird im vorliegenden Artikel ein Clustering von Geschäftsmodellinnovatoren vorgenommen – von Hesitator über Follower über Optimizer bis zu Leader in Geschäftsmodellinnovationen.
2018

Andrusyak Bohdan, Kern Roman, Rimel Myhailo

Detection of abusive speech for mixed sociolects of Russian and Ukrainian language

Tribun Eu, 2018

Konferenz
Uncontrolled use of abusive language is a problem in modern society. Development of automatic tools for detecting abusive and hate speech has been an active research topic in the past decade. However, very little research has been done on this topic for Russian and Ukrainian languages. To our best knowledge, no research considered surzhyk. We propose to use unsupervised probabilistic technique with a seed dictionary for detecting abusive comments in social media in Russian and Ukrainian languages. We demonstrate that this approach is feasible and is able to detect abusive terms that are not present in the seed dictionary
2018

Rexha Andi, Dragoni Mauro , Federici Marco

An Unsupervised Aspect Extraction Strategy For Monitoring Real-Time Reviews Stream

Elsevier, 2018

Journal
One of the most important opinion mining research directions falls in the extraction ofpolarities referring to specific entities (aspects) contained in the analyzed texts. Thedetection of such aspects may be very critical especially when documents come fromunknown domains. Indeed, while in some contexts it is possible to train domainspecificmodels for improving the effectiveness of aspects extraction algorithms, inothers the most suitable solution is to apply unsupervised techniques by making suchalgorithms domain-independent and more efficient in a real-time environment. Moreover,an emerging need is to exploit the results of aspect-based analysis for triggeringactions based on these data. This led to the necessity of providing solutions supportingboth an effective analysis of user-generated content and an efficient and intuitive wayof visualizing collected data. In this work, we implemented an opinion monitoringservice implementing (i) a set of unsupervised strategies for aspect-based opinion miningtogether with (ii) a monitoring tool supporting users in visualizing analyzed data.The aspect extraction strategies are based on the use of an open information extractionstrategy. The effectiveness of the platform has been tested on benchmarks provided by the SemEval campaign and have been compared with the results obtained by domainad aptedtechniques.
2018

Koncar Philipp

Synthetic Dataset for Outlier Detection

Zenodo, 2018

This synthetically generated dataset can be used to evaluate outlier detection algorithms. It has 10 attributes and 1000 observations, of which 100 are labeled as outliers. Two-dimensional combinations of attributes form differently shaped clusters. Attribute 0 & Attribute 1: Two circular clusters Attribute 2 & Attribute 3: Two banana shaped clusters Attribute 4 & Attribute 5: Three point clouds Attribute 6 & Attribute 7: Two point clouds with variances Attribute 8 & Attribute 9: Three anisotropic shaped clusters. The "outlier" column states whether an observation is an outlier or not. Additionally, the .zip file contains 10 stratified randomized train test splits (70% train, 30% test).
2018

Kowald Dominik, Seitlinger Paul , Ley Tobias , Lex Elisabeth

The Impact of Semantic Context Cues on the User Acceptance of Tag Recommendations: An Online Study

Companion Proceedings of the 27th International World Wide Web Conference, ACM, Lyon, France, 2018

Konferenz
In this paper, we present the results of an online study with the aim to shed light on the impact that semantic context cues have on the user acceptance of tag recommendations. Therefore, we conducted a work-integrated social bookmarking scenario with 17 university employees in order to compare the user acceptance of a context-aware tag recommendation algorithm called 3Layers with the user acceptance of a simple popularity-based baseline. In this scenario, we validated and verified the hypothesis that semantic context cues have a higher impact on the user acceptance of tag recommendations in a collaborative tagging setting than in an individual tagging setting. With this paper, we contribute to the sparse line of research presenting online recommendation studies.
2018

Bassa Akim, Kröll Mark, Kern Roman

GerIE - An Open InformationExtraction System for the German Language

Journal of Universal Computer Science, 2018

Journal
Open Information Extraction (OIE) is the task of extracting relations fromtext without the need of domain speci c training data. Currently, most of the researchon OIE is devoted to the English language, but little or no research has been conductedon other languages including German. We tackled this problem and present GerIE, anOIE parser for the German language. Therefore we started by surveying the availableliterature on OIE with a focus on concepts, which may also apply to the Germanlanguage. Our system is built upon the output of a dependency parser, on which anumber of hand crafted rules are executed. For the evaluation we created two dedicateddatasets, one derived from news articles and one based on texts from an encyclopedia.Our system achieves F-measures of up to 0.89 for sentences that have been correctlypreprocessed.
2018

Pammer-Schindler Viktoria, Wertner Alfred, Stern Hermann, Weghofer Franz

Talk2Me – Sprachgesteuerte Kommissionierung mit off-the-shelf Hardware

Beiträge zum Usability Day XVI: Assistenztechnologien in der Arbeitswelt , Patrick Jost, Guido Kempter, PABST SCIENCE PUBLISHERS, 2018

Konferenz
Sprachsteuerung stellt ein potentiell sehr mächtiges Werkzeug dar und sollte rein von der Theorie (grundlegende Spracheingabe) her schon seit 20 Jahren einsetzbar sein. Sie ist in der Vergangenheit im industriellen Umfeld jedoch primär an nicht ausgereifter Hardware oder gar der Notwendigkeit einer firmenexternen aktiven Datenverbindung gescheitert. Bei Magna Steyr am Standort Graz wird die Kommissionierung bisher mit Hilfe von Scan-nern erledigt. Dieser Prozess ließe sich sehr effektiv durch eine durchgängige Sprachsteue-rung unterstützen, wenn diese einfach, zuverlässig sowie Compliance-konform umsetzbar wäre und weiterhin den Menschen als zentralen Mittelpunkt und Akteur (Stichwort Hu-man in the Loop) verstehen würde. Daher wurden bestehende Spracherkennungssysteme für mobile Plattformen sowie passende „off the shelf“ Hardware (Smartphones und Headsets) ausgewählt und prototypisch als Android Applikation („Talk2Me“) umgesetzt. Ziel war es, eine Aussage über die Einsetzbarkeit von sprachgesteuerten mobilen Anwen-dungen im industriellen Umfeld liefern zu können.Mit dem Open Source Speech Recognition Kit CMU Sphinx in Kombination mit speziell auf das Vokabular der abgebildeten Prozesse angepassten Wörterbüchern konnten wir eine sehr gute Erkennungsrate erreichen ohne das Sprachmodell individuell auf einzelne Mitar-beiterInnen trainieren zu müssen. Talk2Me zeigt innovativ, wie erprobte, kostengünstige und verfügbare Technologie (Smartphones und Spracherkennung als Eingabe sowie Sprachsynthese als Ausgabe) Ein-zug in unseren Arbeitsalltag haben kann.
2018

Lassnig Markus, Stabauer Petra, Breitfuß Gert, Mauthner Katrin

Geschäftsmodellinnovationen im Zeitalter von Digitalisierung und Industrie 4.0

HMD Praxis der Wirtschaftsinformatik Wirtschaftsinformatik, Stefan Meinhard, Karl-Michael Popp, Springer Fachmedien Wiesbaden, Wiesbaden, 2018

Journal
Zahlreiche Forschungsergebnisse im Bereich Geschäftsmodellinnovationenhaben gezeigt, dass über 90% aller Geschäftsmodelle der letzten50 Jahre aus einer Rekombination von bestehenden Konzepten entstanden sind.Grundsätzlich gilt das auch für digitale Geschäftsmodellinnovationen. Angesichtsder Breite potenzieller digitaler Geschäftsmodellinnovationen wollten die Autorenwissen, welche Modellmuster in der wirtschaftlichen Praxis welche Bedeutung haben.Deshalb wurde die digitale Transformation mit neuen Geschäftsmodellen ineiner empirischen Studie basierend auf qualitativen Interviews mit 68 Unternehmenuntersucht. Dabei wurden sieben geeignete Geschäftsmodellmuster identifiziert, bezüglichihres Disruptionspotenzials von evolutionär bis revolutionär klassifiziert undder Realisierungsgrad in den Unternehmen analysiert.Die stark komprimierte Conclusio lautet, dass das Thema Geschäftsmodellinnovationendurch Industrie 4.0 und digitale Transformation bei den Unternehmenangekommen ist. Es gibt jedoch sehr unterschiedliche Geschwindigkeiten in der Umsetzungund im Neuheitsgrad der Geschäftsmodellideen. Die schrittweise Weiterentwicklungvon Geschäftsmodellen (evolutionär) wird von den meisten Unternehmenbevorzugt, da hier die grundsätzliche Art und Weise des Leistungsangebots bestehenbleibt. Im Gegensatz dazu gibt es aber auch Unternehmen, die bereits radikale Änderungenvornehmen, die die gesamte Geschäftslogik betreffen. Entsprechend wird imvorliegenden Artikel ein Clustering von Geschäftsmodellinnovatoren vorgenommen – von Hesitator über Follower über Optimizer bis zu Leader in Geschäftsmodellinnovationen
2018

Lovric Mario, Molero Perez Jose Manuel, Kern Roman

PySpark and RDKit: moving towards Big Data in QSAR

Molecular Informatics, Wiley, 2018

Journal
We present an implementation of the cheminformatics toolkit RDKit in a distributed computing environment, Apache Hadoop. Together with the Apache Spark analytics engine, wrapped in PySpark, resources from commodity scalable hardware can be used for cheminformatic calculations and query operations with basic knowledge in Python coding and understanding of the RDD abstraction. A comparison of the computing acceleration in the Hadoop cluster is presented in two computation tasks of querying substructures and calculating molecular descriptors, as well as the source code for the PySpark-RDKit implementation
2018

Santos Tiago, Walk Simon, Kern Roman, Helic Denis

Evolution of Collaborative Web Communities

ACM Hypertext 2018, 2018

Konferenz
Each day, millions of users visit collaborative Web communities, such as Wikipedia or StackExchange, either as large knowledge repositories or as up-to-date news sources.However, not all of Web communities are as successful as Wikipedia and, except for a few initial research results, our research community still knows only a little about what separates a successful from an unsuccessful community.Thus, we still need to (i) gain a better understanding of the underlying community evolution dynamics, and (ii) based on this understanding support activity and growth on such platforms.To that end, we distill temporal dynamics of community activity and thereby identify key factors leading to success or failure of communities.In particular, we study the differences between growing and declining communities by leveraging multivariate Hawkes processes. Furthermore, we compare communities hosted on different platforms such as StackExchange and Reddit, as well as topically diverse communities such as STEM and humanities.We find that all growing communities exhibit (i) an active core of power users reacting to the community as a whole, and (ii) numerous casual users strongly interacting with other casual users suggesting community openness towards less active users.Moreover, our results suggest that communities in the humanities are centered around power users, whereas in STEM communities activity is more evenly distributed among power and casual users.These results are of practical importance for community managers to quantitatively assess the status of their communities and guide them towards thriving community structures
2018

Lacic Emanuel, Traub Matthias, Duricic Tomislav, Haslauer Eva, Lex Elisabeth

Gone in 30 Days! Predictions for Car Import Planning

it - Information Technology, De Gruyter Oldenbourg, 2018

Journal
A challenge for importers in the automobile industry is adjusting to rapidly changing market demands. In this work, we describe a practical study of car import planning based on the monthly car registrations in Austria. We model the task as a data driven forecasting problem and we implement four different prediction approaches. One utilizes a seasonal ARIMA model, while the other is based on LSTM-RNN and both compared to a linear and seasonal baselines. In our experiments, we evaluate the 33 different brands by predicting the number of registrations for the next month and for the year to come.
2018

Andrusyak Bohdan, Kugi Thomas, Kern Roman

Daily Prediction of Foreign Exchange Rates Based on the Stock Marke

Proceedings of the PEFNet 2017 conference, Jana Stávková, Mendel University Press, Brno, 2018

Konferenz
The stock and foreign exchange markets are the two fundamental financial markets in the world and play acrucial role in international business. This paper examines the possibility of predicting the foreign exchangemarket via machine learning techniques, taking the stock market into account. We compare prediction modelsbased on algorithms from the fields of shallow and deep learning. Our models of foreign exchange marketsbased on information from the stock market have been shown to be able to predict the future of foreignexchange markets with an accuracy of over 60%. This can be seen as an indicator of a strong link between thetwo markets. Our insights offer a chance of a better understanding guiding the future of market predictions.We found the accuracy depends on the time frame of the forecast and the algorithms used, where deeplearning tends to perform better for farther-reaching forecasts
2018

Cuder Gerald, Breitfuß Gert, Kern Roman

E-Mobility and Big Data - Data Utilization of Charging Operations

Proceedings of XXIX ISPIM Conference, Stockholm, 2018

Konferenz
Electric vehicles have enjoyed a substantial growth in recent years. One essential part to ensure their success in the future is a well-developed and easy-to-use charging infrastructure. Since charging stations generate a lot of (big) data, gaining useful information out of this data can help to push the transition to E-Mobility. In a joint research project, the Know-Center, together with the has.to.be GmbH applied data analytics methods and visualization technologies on the provided data sets. One objective of the research project is, to provide a consumption forecast based on the historical consumption data. Based on this information, the operators of charging stations are able to optimize the energy supply. Additionally, the infrastructure data were analysed with regard to "predictive maintenance", aiming to optimize the availability of the charging stations. Furthermore, advanced prediction algorithms were applied to provide services to the end user regarding availability of charging stations.
2018

Fruhwirth Michael, Breitfuß Gert, Pammer-Schindler Viktoria

Exploring challenges in data-driven business model innovation from Austrian enterprises

Proceedings in XXIX ISPIM Innovation Conference, Stockholm, 2018

Konferenz
The increasing amount of generated data and advances in technology and data analytics and are enablers and drivers for new business models with data as a key resource. Currently established organisations struggle with identifying the value and benefits of data and have a lack of know-how, how to develop new products and services based on data. There is very little research that is narrowly focused on data-driven business model innovation in established organisations. The aim of this research is to investigate existing activities within Austrians enterprises with regard to exploring data-driven business models and challenges encountered in this endeavour. The outcome of the research in progress paper are categories of challenges related to organisation, business and technology, established organisations in Austria face during data-driven business model innovation
2018

Ross-Hellauer Anthony, Kowald Dominik, Lex Elisabeth

Recommender Systems as Enabling Technology to Interlink Scholarly Information

Scholarly Communication Workshop co-located with WWW'2018, Lyon, 2018

Konferenz
2018

Kowald Dominik

Modeling Activation Processes in HumanMemory to Improve Tag Recommendation

SIGIR Newsletter, ACM, 2018

Journal
Social tagging systems enable users to collaboratively assign freely chosen keywords (i.e.,tags) to resources (e.g., Web links). In order to support users in nding descriptive tags, tagrecommendation algorithms have been proposed. One issue of current state-of-the-art tagrecommendation algorithms is that they are often designed in a purely data-driven way andthus, lack a thorough understanding of the cognitive processes that play a role when peopleassign tags to resources. A prominent example is the activation equation of the cognitivearchitecture ACT-R, which formalizes activation processes in human memory to determineif a speci c memory unit (e.g., a word or tag) will be needed in a speci c context. It is theaim of this thesis to investigate if a cognitive-inspired approach, which models activationprocesses in human memory, can improve tag recommendations.For this, the relation between activation processes in human memory and usage prac-tices of tags is studied, which reveals that (i) past usage frequency, (ii) recency, and (iii)semantic context cues are important factors when people reuse tags. Based on this, acognitive-inspired tag recommendation approach termed BLLAC+MPr is developed based onthe activation equation of ACT-R. An extensive evaluation using six real-world folksonomydatasets shows that BLLAC+MPr outperforms current state-of-the-art tag recommendationalgorithms with respect to various evaluation metrics. Finally, BLLAC+MPr is utilized forhashtag recommendations in Twitter to demonstrate its generalizability in related areas oftag-based recommender systems. The ndings of this thesis demonstrate that activationprocesses in human memory can be utilized to improve not only social tag recommendationsbut also hashtag recommendations. This opens up a number of possible research strands forfuture work, such as the design of cognitive-inspired resource recommender systems
2018

Kaiser René

Towards Applying the Virtual Director Concept to 360 Degree Video Content

Adjunct Publication of the 2018 ACM International Conference on Interactive Experiences for TV and Online Video, Figshare, Seoul, South Korea, 2018

Konferenz
This paper aims to contribute to the discussion on 360° video storytelling. It describes the 'Virtual Director' concept, an enabling technology that was developed to personalize video presentation in applications where multiple live streams are available at the same time. Users are supported in dynamically changing viewpoints, as the Virtual Director essentially automates the tasks of a human director. As research prototypes on a proof-of-concept maturity level, this approach has been evaluated for personalized live event broadcast, group video communication and distributed theatre performances. While on the capture side a 180° high-resolution panoramic video feed has been used in one of these application scenarios, so far, only traditional 2D video screen were investigated for playout. The research question this paper aims to contribute to is how technology in general, and an adaptation of the Virtual Director concept in particular, could assist users in their needs when consuming 360° content, both live and recorded. In contexts when users do not want to enjoy the freedom to look into any direction, or when content creators want them to look in a certain direction, how could the interaction with and intervention of a Virtual Director be applied from a storytelling point of view?
2018

Lovric Mario

Chemical outlier dataset

Zenodo, 2018

The objects are numbered. The Y-variable are boiling points. Other features are structural features of molecules. In the outlier column the outliers are assigned with a value of 1.The data is derived from a published chemical dataset on boiling point measurements [1] and from public data [2]. Features were generated by means of the RDKit Python library [3]. The dataset was infused with known outliers (~5%) based on significant structural differences, i.e. polar and non-polar molecules. Cherqaoui D., Villemin D. Use of a Neural Network to determine the Boiling Point of Alkanes. J CHEM SOC FARADAY TRANS. 1994;90(1):97–102. https://pubchem.ncbi.nlm.nih.gov/ RDKit: Open-source cheminformatics; http://www.rdkit.org
2018

Lovric Mario, Stipaničev Draženka , Repec Siniša , Malev Olga , Klobučar Göran

Combined toxic unit: Moving towards a multipath risk assessment strategy of organic contaminants in river sediment

The International Water Association, 2018

Konferenz
2018

Neuhold Robert, Gursch Heimo, Kern Roman, Cik Michael

Driver's Dashboard - Using Social Media Data as additional Information for Motorway Operators

Proceedings of the ITS World Congress 2018, Intelligent Transportation Society, Copenhagen, Denmark, 2018

Konferenz
Data collection on motorways for traffic management operations is traditionally based on local measurements points and camera monitoring systems. This work looks into social media as additional data source for the Austrian motorway operator ASFINAG. A system called Driver´s Dashboard was developed collecting incident descriptions from Facebook and RSS feeds, filtering relevant messages, and fusing them with traffic data. All collected texts were analysed for concepts describing road situations linking the texts from the web and social media with traffic messages and traffic data. Driver´s Dashboard was designed to examine the potential of social media for traffic monitoring with respect to Austrian characteristics in social media use and road transportation with only very few messages are available compared to other studies. Of 3,586 messages collected within a five-week period only 7.1% were automatically annotated as traffic relevant. Further, the traffic relevant messages for the motorway operator were analysed more in detail to identify correlations between message text and traffic data characteristics. A correlation of message text and traffic data was found in nine of eleven messages by comparing the speed profiles and traffic state data with the message text.
2018

Breitfuß Gert, Berger Martin, Doerrzapf Linda

Innovation Milieus for Mobility – Analysis of Innovation Lab Approaches for the Establishment of Urban Mobility Labs in Austria

TRA Vienna 2018 - Transport Research Arena, 2018

Konferenz
The initiative „Urban Mobility Labs“ (UML), driven by the Austrian Ministry of Transport, Innovation and Technology, was started to support the setup of innovative and experimental environments for research, testing, implementation and transfer of mobility solutions. This should happen by incorporating the scientific community, citizens and stakeholders in politics and administration as well as other groups. The emerging structural frame shall enhance the efficiency and effectivity of the innovation process. In this paper insights and in-depth analysis of the approaches and experiences gained in the eight UML exploratory projects will be outlined. These projects were analyzed, systematized and enriched with further considerations. Furthermore, their knowledge growth as user-centered innovation environments was documented during the exploratory phase.
2018

Arslanovic Jasmina, Lovric Mario, Kern Roman

EATING DISORDERS IN SYNCHRONIZED SWIMMING

2018

Journal
The aim of the present study was to identify eating disorders in synchronized swimming and the level of distortion of the body image of synchronized swimming athletes. Synchronized swimming is sport in which modality is considered of risk for development of eating disorder. It is a Olympic sport where synchronized swimmers are competing in the range of age 13-15 years old and that ages are critical for every young woman (puberty). Also, the beauty of movement is associated to low body mass and judges include thinness in their final score. Eating disorders include anorexia nervosa, bulimia nervosa, binge eating symptoms, and other specified (or non-specified) feeding or eating disorders which are presenting serious issue. Twenty synchronized swimmers age 13-16 years old was studied and the group of twenty female water polo players was used for comparison with athletes. To test the presence of some symptoms of the eating disorders was used The Eating Attitudes Test: “Eat26” (Garner and Garfunkel, 1979). Comparison of results have shown that statistically significant difference exist between synchronized swimmers and female water polo players in the image of dissatisfaction, with pathological control of body weight of synchronized swimmers. Almost every sport demands certain nutrition and most of them certain weight, but that should be achieved with the help of expert team. It is possible that synchronized swimmers are skinny and strong, but only with certain nutrition which is individual for every swimmer.
2018

Babić Sanja, Barišić Josip, Stipaničev Draženka, Repec Siniša, Lovric Mario, Malev Olga, Čož-Rakovac Rozalindra, Klobučar GIV

Assessment of river sediment toxicity: Combining empirical zebrafish embryotoxicity testing with in silico toxicity characterization

Science of the Total Environment, Elsevier, 2018

Journal
Quantitative chemical analyses of 428 organic contaminants (OCs) confirmed the presence of 313 OCs in the sediment extracts from river Sava, Croatia. Pharmaceuticals were present in higher concentration than pesticides thus confirming their increasing threat to freshwater ecosystems. Toxicity evaluation of the sediment extracts from four locations (Jesenice, Rugvica, Galdovo and Lukavec) using zebrafish embryotoxicity test (ZET) accompanied with semi-quantitative histopathological analyses exhibited good correlation with cumulative number and concentrations of OCs at investigated sites (10,048.6, 15,222.8, 1,247.6, and 9,130.5 ng/g respectively) and proved its role as a good indicator of toxic potential of complex contaminant mixtures. Toxicity prediction of sediment extracts and sediment was assessed using Toxic unit (TU) approach and PBT (persistence, bioaccumulation and toxicity) ranking. Also, prior-knowledge informed chemical-gene interaction models were generated and graph mining approaches used to identify OCs and genes most likely to be influential in these mixtures. Predicted toxicity of sediment extracts (TUext) for sampled locations was similar to the results obtained by ZET and associated histopathology resulting in Rugvica sediment as being the most toxic, followed by Jesenice, Lukavec and Galdovo. Sediment TU (TUsed) favoured OCs with low octanol-water partition coefficient like herbicide glyphosate and antibiotics ciprofloxacin and sulfamethazine thus indicating locations containing higher concentrations of these OCs (Galdovo and Rugvica) as most toxic. Results suggest that comprehensive in silico sediment toxicity predictions advocate providing equal attention to organic contaminants with either very low or very high log Kow
2018

Bassa Kevin, Kern Roman, Kröll Mark

On-the-fly Data Set Generation for Single Fact Validation

SAC 2018, 2018

Konferenz
On the web, massive amounts of information are available, includingwrong (or conflicting) information. This spreading of erroneous or fake contentsmakes it hard for users to distinguish between what is true and what is not. Factfinding algorithms represent a means to validate information. Yet, these algorithmsrequire an already existing, structured data set to validate a single fact; anad-hoc validation is thus not supported making them impractical for usage in realworld applications. This work presents an approach to generate these data setson-the-fly. For three facts, we generate respective data sets and apply six state-ofthe-art fact finding algorithms for evaluation purposes. In addition, our approachcontributes to comparing fact finding algorithms in a more objective way.
2018

Rexha Andi, Kröll Mark, Kern Roman

Multilingual Open Information Extraction using Parallel Corpora: The German Language Case

ACM Symposium on Applied Computing , Hisham M. Haddad, Roger L. Wainwright, ACM, 2018

Konferenz
In the past decade the research community has been continuously improving theextraction quality of Open Information Extraction systems. This was done mainlyfor the English language; other languages such as German or Spanish followedusing shallow or deep parsing information to derive language-specific patterns.More recent efforts focused on language agnostic approaches in an attempt tobecome less dependent on available tools and resources in that language. In linewith these efforts, we present a language agnostic approach which exploitsmanually aligned corpora as well as the solid performance of English OpenIEtools.
2018

Rexha Andi, Kröll Mark, Ziak Hermann, Kern Roman

Authorship Identification of Documents with High Content Similarity

Scientometrics, Wolfgang Glänzel, Springer Link, 2018

Journal
The goal of our work is inspired by the task of associating segments of text to their real authors. In this work, we focus on analyzing the way humans judge different writing styles. This analysis can help to better understand this process and to thus simulate/ mimic such behavior accordingly. Unlike the majority of the work done in this field (i.e., authorship attribution, plagiarism detection, etc.) which uses content features, we focus only on the stylometric, i.e. content-agnostic, characteristics of authors.Therefore, we conducted two pilot studies to determine, if humans can identify authorship among documents with high content similarity. The first was a quantitative experiment involving crowd-sourcing, while the second was a qualitative one executed by the authors of this paper.Both studies confirmed that this task is quite challenging.To gain a better understanding of how humans tackle such a problem, we conducted an exploratory data analysis on the results of the studies. In the first experiment, we compared the decisions against content features and stylometric features. While in the second, the evaluators described the process and the features on which their judgment was based. The findings of our detailed analysis could (i) help to improve algorithms such as automatic authorship attribution as well as plagiarism detection, (ii) assist forensic experts or linguists to create profiles of writers, (iii) support intelligence applications to analyze aggressive and threatening messages and (iv) help editor conformity by adhering to, for instance, journal specific writing style.
2018

Hojas Sebastian, Kröll Mark, Kern Roman

GerMeter - A Corpus for Measuring Text Reuse in the Austrian JournalisticDomain

Language Resources and Evaluation, Springer, 2018

Journal
2018

Urak Günter, Ziak Hermann, Kern Roman

Source Selection of Long Tail Sources for Federated Search in an Uncooperative Setting

SAC, 2018

Konferenz
The task of federated search is to combine results from multiple knowledge bases into a single, aggregated result list, where the items typically range from textual documents toimages. These knowledge bases are also called sources, and the process of choosing the actual subset of sources for a given query is called source selection. A scenario wherethese sources do not provide information about their content in a standardized way is called uncooperative setting. In our work we focus on knowledge bases providing long tail content, i.e., rather specialized sources offering a low number of relevant documents. These sources are often neglected in favor of more popular knowledge sources, both by today’s Web users as well as by most of the existing source selection techniques. We propose a system for source selection which i) could be utilized to automatically detect long tail knowledge bases and ii) generates aggregated search results that tend to incorporate results from these long tail sources. Starting from the current state-of-the-art we developed components that allowed to adjust the amount of contribution from long tail sources. Our evaluation is conducted on theTREC 2014 Federated WebSearch dataset. As this dataset also favors the most popular sources, systems that include many long tail knowledge bases will yield low performancemeasures. Here, we propose a system where just a few relevant long tail sources are integrated into the list of more popular knowledge bases. Additionally, we evaluated the implications of an uncooperative setting, where only minimal information of the sources is available to the federated search system. Here a severe drop in performance is observed once the share of long tail sources is higher than 40%. Our work is intended to steer the development of federated search systems that aim at increasing the diversity and coverage of the aggregated search result.
2018

Lacic Emanuel, Kowald Dominik, Reiter-Haas Markus, Slawicek Valentin, Lex Elisabeth

Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendation

In Proceedings of the International Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization (IFUP'2018) co-located with the 11th ACM International Conference on Web Search and Data Mining, WSDM'2018, ACM, Los Angeles, USA, 2018

Konferenz
In this work, we address the problem of recommending jobs touniversity students. For this, we explore the impact of using itemembeddings for a content-based job recommendation system. Fur-thermore, we utilize a model from human memory theory to integratethe factors of frequency and recency of job posting interactions forcombining item embeddings. We evaluate our job recommendationsystem on a dataset of the Austrian student job portal Studo usingprediction accuracy, diversity as well as adapted novelty, which isintroduced in this work. We find that utilizing frequency and recencyof interactions with job postings for combining item embeddingsresults in a robust model with respect to accuracy and diversity, butalso provides the best adapted novelty results
2018

Santos Tiago, Kern Roman

Understanding semiconductor production with variational auto-encoders

European Symposium on Artificial Neural Network (ESANN) 2018, 2018

Konferenz
Semiconductor manufacturing processes critically depend on hundreds of highly complex process steps, which may cause critical deviations in the end-product.Hence, a better understanding of wafer test data patterns, which represent stress tests conducted on devices in semiconductor material slices, may lead to an improved production process.However, the shapes and types of these wafer patterns, as well as their relation to single process steps, are unknown.In a first step to address these issues, we tailor and apply a variational auto-encoder (VAE) to wafer pattern images.We find the VAE's generator allows for explorative wafer pattern analysis, andits encoder provides an effective dimensionality reduction algorithm, which, in a clustering application, performs better than several baselines such as t-SNE and yields interpretable clusters of wafer patterns.
2018

Lovric Mario

Molecular modeling of the quantitative structure activity relationship in Python – a tutorial (part I)

Journal of Chemists and Chemical Engineers, Croatian Society of Chemical Engineers, Zagreb, 2018

Journal
Today's data amount is significantly increasing. A strong buzzword in research nowadays is big data.Therefore the chemistry student has to be well prepared for the upcoming age where he does not only rule the laboratories but is a modeler and data scientist as well. This tutorial covers the very basics of molecular modeling and data handling with the use of Python and Jupyter Notebook. It is the first in a series aiming to cover the relevant topics in machine learning, QSAR and molecular modeling, as well as the basics of Python programming
2018

Lovric Mario, Krebs Sarah, Cemernek David, Kern Roman

BIG DATA IN INDUSTRIAL APPLICATION

XII Meeting of Young Chemical Engineers, Zagreb, Kroatien, 2018

Konferenz
The use of big data technologies has a deep impact on today’s research (Tetko et al., 2016) and industry (Li et al., n.d.), but also on public health (Khoury and Ioannidis, 2014) and economy (Einav and Levin, 2014). These technologies are particularly important for manufacturing sites, where complex processes are coupled with large amounts of data, for example in chemical and steel industry. This data originates from sensors, processes. and quality-testing. Typical application of these technologies is related to predictive maintenance and optimisation of production processes. Media makes the term “big data” a hot buzzword without going to deep into the topic. We noted a lack in user’s understanding of the technologies and techniques behind it, making the application of such technologies challenging. In practice the data is often unstructured (Gandomi and Haider, 2015) and a lot of resources are devoted to cleaning and preparation, but also to understanding causalities and relevance among features. The latter one requires domain knowledge, making big data projects not only challenging from a technical perspective, but also from a communication perspective. Therefore, there is a need to rethink the big data concept among researchers and manufacturing experts including topics like data quality, knowledge exchange and technology required. The scope of this presentation is to present the main pitfalls in applying big data technologies amongst users from industry, explain scaling principles in big data projects, and demonstrate common challenges in an industrial big data project
2018

Santos Tiago, Walk Simon, Kern Roman, Strohmaier M., Helic Denis

Activity in Questions & Answers Websites

ACM Transactions on Social Computing, 2018

Journal
Millions of users on the Internet discuss a variety of topics on Question and Answer (Q&A) instances. However, not all instances and topics receive the same amount of attention, as some thrive and achieve self-sustaining levels of activity while others fail to attract users and either never grow beyond being a small niche community or become inactive. Hence, it is imperative to not only better understand but also to distill deciding factors and rules that define and govern sustainable Q&A instances. We aim to empower community managers with quantitative methods for them to better understand, control and foster their communities, and thus contribute to making the Web a more efficient place to exchange information. To that end, we extract, model and cluster user activity-based time series from 50 randomly selected Q&A instances from the StackExchange network to characterize user behavior. We find four distinct types of user activity temporal patterns, which vary primarily according to the users' activity frequency. Finally, by breaking down total activity in our 50 Q&A instances by the previously identified user activity profiles, we classify those 50 Q&A instances into three different activity profiles. Our categorization of Q&A instances aligns with the stage of development and maturity of the underlying communities, which can potentially help operators of such instances not only to quantitatively assess status and progress, but also allow them to optimize community building efforts
2018

Luzhnica Granit, Veas Eduardo Enrique

Investigating Interactions for Text Recognition using a Vibrotactile Wearable Display

ACM International Conference on Intelligent User Interfaces , Tokyo, 2018

Konferenz
Vibrotactile skin-reading uses wearable vibrotactile displays to convey dynamically generated textual information. Such wearable displays have potential to be used in a broad range of applications. Nevertheless, the reading process is passive, and users have no control over the reading flow. To compensate for such drawback, this paper investigates what kind of interactions are necessary for vibrotactile skin reading and the modalities of such interactions. An interaction concept for skin reading was designed by taking into account the reading as a process. We performed a formative study with 22 participants to assess reading behaviour in word and sentence reading using a six-channel wearable vibrotactile display. Our study shows that word based interactions in sentence reading are more often used and preferred by users compared to character-based interactions and that users prefer gesture-based interaction for skin reading. Finally, we discuss how such wearable vibrotactile displays could be extended with sensors that would enable recognition of such gesture-based interaction. This paper contributes a set of guidelines for the design of wearable haptic displays for text communication.
2018

Hasani-Mavriqi Ilire, Kowald Dominik, Helic Denis, Lex Elisabeth

Consensus Dynamics in Online Collaboration Systems

Journal of Computational Social Networks , Ding-Zhu Du and My T. Thai, Springer Open, 2018

Journal
In this paper, we study the process of opinion dynamics and consensus building inonline collaboration systems, in which users interact with each other followingtheir common interests and their social pro les. Speci cally, we are interested inhow users similarity and their social status in the community, as well as theinterplay of those two factors inuence the process of consensus dynamics. Forour study, we simulate the di usion of opinions in collaboration systems using thewell-known Naming Game model, which we extend by incorporating aninteraction mechanism based on user similarity and user social status. Weconduct our experiments on collaborative datasets extracted from the Web. Our ndings reveal that when users are guided by their similarity to other users, theprocess of consensus building in online collaboration systems is delayed. Asuitable increase of inuence of user social status on their actions can in turnfacilitate this process. In summary, our results suggest that achieving an optimalconsensus building process in collaboration systems requires an appropriatebalance between those two factors.
2018

Kaiser René

Opportunities and Challenges of Video Content and Video Technology in Smart Factories

Patrick Jost, Guido Kempter, Pabst Science Publishers, Dornbirn, AT, 2018

Konferenz
Production companies typically have not utilized video content and video technology in factory environ-ments to a significant extent in the past. However, the current Industry 4.0 movement inspires companies to reconsider production processes and job qualifications for their shop floor workforce. Infrastructure and machines get connected to central manufacturing execution systems in digitization and datafication efforts. In the realm of this fourth industrial revolution, companies are encouraged to revisit their strategy regarding video-based applications as well. This paper discusses the current situation and selected aspects of opportu-nities and challenges of video technology that might enable added value in such environments.
2018

d'Aquin Mathieu , Kowald Dominik, Fessl Angela, Thalmann Stefan, Lex Elisabeth

AFEL - Analytics for Everyday Learning

Proceedings of the International Projects Track co-located with the 27th International World Wide Web Conference, ACM, Lyon, France, 2018

Konferenz
The goal of AFEL is to develop, pilot and evaluate methods and applications, which advance informal/collective learning as it surfaces implicitly in online social environments. The project is following a multi-disciplinary, industry-driven approach to the analysis and understanding of learner data in order to personalize, accelerate and improve informal learning processes. Learning Analytics and Educational Data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners' behaviours. However, studies have for a long time demonstrated that learning activities happen outside of formal educational platforms, also. This includes informal and collective learning usually associated, as a side effect, with other (social) environments and activities. Relying on real data from a commercially available platform, the aim of AFEL is to provide and validate the technological grounding and tools for exploiting learning analytics on such learning activities. This will be achieved in relation to cognitive models of learning and collaboration, which are necessary to the understanding of loosely defined learning processes in online social environments. Applying the skills available in the consortium to a concrete set of live, industrial online social environments, AFEL will tackle the main challenges of informal learning analytics through 1) developing the tools and techniques necessary to capture information about learning activities from (not necessarily educational) online social environments; 2) creating methods for the analysis of such informal learning data, based on combining feature engineering and visual analytics with cognitive models of learning and collaboration; and 3) demonstrating the potential of the approach in improving the understanding of informal learning, and the way it is better supported; 4) evaluate all the former items in real world large scale applications and platforms.
2018

Pammer-Schindler Viktoria, Thalmann Stefan, Fessl Angela, Füssel Julia

Virtualizing face-2-face trainings for training senior professionals: A Comparative Case Study on Financial Auditor

AC, London, 2018

Konferenz
Traditionally, professional learning for senior professionalsis organized around face-2-face trainings. Virtual trainingsseem to offer an opportunity to reduce costs related to traveland travel time. In this paper we present a comparative casestudy that investigates the differences between traditionalface-2-face trainings in physical reality, and virtualtrainings via WebEx. Our goal is to identify how the way ofcommunication impacts interaction between trainees,between trainees and trainers, and how it impactsinterruptions. We present qualitative results fromobservations and interviews of three cases in differentsetups (traditional classroom, web-based with allparticipants co-located, web-based with all participants atdifferent locations) and with overall 25 training participantsand three trainers. The study is set within one of the BigFour global auditing companies, with advanced seniorauditors as learning cohort
2018

Fernández Alonso, Miguel Yuste, Kern Roman

Tinkerforge environmental datasets

2018

Collection of environmental datasets recorded with Tinkerforge sensors and used in the development of a bachelor thesis on the topic of frequent pattern mining. The data was collected in several locations in the city of Graz, Austria, as well as an additional dataset recorded in Santander, Spain.
2018

Luzhnica Granit, Veas Eduardo Enrique, Caitlyn Seim

Passive Haptic Learning for Vibrotactile Skin Reading

ACM International Symposium on Wearable Computing ISWC, ACM, 2018

Konferenz
This paper investigates the effects of using passive haptic learning to train the skill of comprehending text from vibrotactile patterns. The method of transmitting messages, skin-reading, is effective at conveying rich information but its active training method requires full user attention, is demanding, time-consuming, and tedious. Passive haptic learning offers the possibility to learn in the background while performing another primary task. We present a study investigating the use of passive haptic learning to train for skin-reading.
2018

Barreiros Carla, Veas Eduardo Enrique, Pammer-Schindler_TU Viktoria

Bringing Nature into Our Lives

Human-Computer Interaction, Lecture Notes in Computer Science, Springer International Publishing, 2018

Konferenz
In the context of the Internet of Things (IoT), every device have sensing and computing capabilities to enhance many aspects of human life. There are more and more IoT devices in our homes and at our workplaces, and they still depend on human expertise and intervention for tasks as maintenance and (re)configuration.
2018

Iacopo Vagliano, Franziska Günther, Mathias Heinz, Aitor Apaolaza, Irina Bienia, Breitfuß Gert, Till Blume, Chrysa Collyda, Fessl Angela, Sebastian Gottfried, Hasitschka Peter, Jasmin Kellermann, Thomas Köhler, Annalouise Maas, Vasileios Mezaris, Ahmed Saleh, Andrzej Skulimowski, Thalmann_TU Stefan, Markel Vigo, Wertner Alfred, Michael Wiese, Ansgar Scherp

Open Innovation in the Big Data Era with the MOVING Platform: An Integrated Working and Training Approach for Data-savvy Information Professionals

IEEE Mulitmedia, 2018

Journal
In the Big Data era, people can access vast amounts of information, but often lack the time, strategies and tools to efficiently extract the necessary knowledge from it. Research and innovation staff needs to effectively obtain an overview of publications, patents, funding opportunities, etc., to derive an innovation strategy. The MOVING platform enables its users to improve their information literacy by training how to exploit data mining methods in their daily research tasks. Through a novel integrated working and training environment, the platform supports the education of data-savvy information professionals and enables them to deal with the challenges of Big Data and open innovation.
2018

Fessl Angela, Wertner Alfred, Pammer-Schindler_TU Viktoria

Challenges in Developing Automatic Learning Guidance in Relation to an Information Literacy Curriculum

CEUR Workshop Proceedings, Angela Fessl, Stefan Thalmann, Mathieu d'Aquin, Peter Holtz, Stefan Dietze , 2018

Konferenz
Becoming a data-savvy professional requires skills and competencesin information literacy, communication and collaboration, and content creationin digital environments. In this paper, we present a concept for automatic learningguidance in relation to an information literacy curriculum. The learning guidanceconcept has three components: Firstly, an open learner model in terms of an informationliteracy curriculum is created. Based on the data collected in the learnermodel, learning analytics is used in combination with a corresponding visualizationto present the current learning status of the learner. Secondly, reflectionprompts in form of sentence starters or reflective questions adaptive to the learnermodel aim to guide learning. Thirdly, learning resources are suggested that arestructured along learning goals to motivate learners to progress. The main contributionof this paper is to discuss what we see as main research challenges withrespect to existing literature on open learner modeling, learning analytics, recommendersystems for learning, and learning guidance.
2018

Fessl Angela, Kowald Dominik, Susana López Sola, Ana Moreno, Ricardo Alonso, Maturana, Thalmann_TU Stefan

Analytics for Everyday Learning from two Perspectives: Knowledge Workers and Teachers.

CEUR Workshop Proceedings, Angela Fessl, Stefan Thalmann, Mathieu d'Aquin, Peter Holtz, Stefan Dietze, 2018

Konferenz
Learning analytics deals with tools and methods for analyzing anddetecting patterns in order to support learners while learning in formal as wellas informal learning settings. In this work, we present the results of two focusgroups in which the effects of a learning resource recommender system and adashboard based on analytics for everyday learning were discussed from twoperspectives: (1) knowledge workers as self-regulated everyday learners (i.e.,informal learning) and (2) teachers who serve as instructors for learners (i.e.,formal learning). Our findings show that the advantages of analytics for everydaylearning are three-fold: (1) it can enhance the motivation to learn, (2) it canmake learning easier and broadens the scope of learning, and (3) it helps to organizeand to systematize everyday learning.
2018

di Sciascio Maria Cecilia, Brusilovsky Peter, Veas Eduardo Enrique

A Study on User-Controllable Social Exploratory Search

ACM Conference on Intelligent User Interfaces IUI, ACM, 2018

Konferenz
Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial-and-error and on-the-fly selections, gathers and organizes information (resources). A range of innovative interfaces with increased user control have been developed to support exploratory search process. In this work we present our attempt to increase the power of exploratory search interfaces by using ideas of social search, i.e., leveraging information left by past users of information systems. Social search technologies are highly popular nowadays, especially for improving ranking. However, current approaches to social ranking do not allow users to decide to what extent social information should be taken into account for result ranking. This paper presents an interface that integrates social search functionality into an exploratory search system in a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates control features that allow the user to (i) express information needs by selecting keywords and (ii) to express preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes search transparency through color-coded stacked bars and rich tooltips. In an online study investigating system accuracy and subjective aspects with a structural model we found that, when users actively interacted with all its control features, the hybrid system outperformed a baseline content-based-only tool and users were more satisfied.
2018

Rexha Andi, Kröll Mark, Kern Roman, Dragoni Mauro

The CLAUSY System at ESWC-2018 Challenge on Semantic Sentiment Analysis

Springer, 2018

Konferenz
With different social media and commercial platforms, users express their opinion about products in a textual form. Automatically extracting the polarity(i.e. whether the opinion is positive or negative) of a user can be useful for both actors: the online platform incorporating the feedback to improve their product as well as the client who might get recommendations according to his or her preferences. Different approaches for tackling the problem, have been suggested mainly using syntactic features. The “Challenge on Semantic Sentiment Analysis” aims to go beyond the word-level analysis by using semantic information. In this paper we propose a novel approach by employing the semantic information of grammatical unit called preposition. We try to derive the target of the review from the summary information, which serves as an input to identify the proposition in it. Our implementation relies on the hypothesis that the proposition expressing the target of the summary, usually containing the main polarity information.
2018

Lex Elisabeth, Wagner Mario, Kowald Dominik

Mitigating Confirmation Bias on Twitter by Recommending Opposing View

European Computational Social Sciences Symposium, 2018

Konferenz
In this work, we propose a content-based recommendation approach to increase exposure to opposing beliefs and opinions. Our aim is to help provide users with more diverse viewpoints on issues, which are discussed in partisan groups from different perspectives. Since due to the backfire effect, people's original beliefs tend to strengthen when challenged with counter evidence, we need to expose them to opposing viewpoints at the right time. The preliminary work presented here describes our first step into this direction. As illustrative showcase, we take the political debate on Twitter around the presidency of Donald Trump.
2018

Rexha Andi, Mauro Dragoni, Marco Federici

ReUS: A Real-Time Unsupervised System For Monitoring Opinion Streams

2018

Journal
Background An actual challenge within the sentimentanalysis research area is the extraction of polarity valuesassociated with specic aspects (or opinion targets)contained in user generated content. This task, calledaspect-based sentiment analysis bring new challengeslike the disambiguation of words' role within text andthe inference of correct polarity values based on thedomain in which a text occurs. The former requiresstrategies able to understand how each word is usedin a specic context in order to annotate it as aspector not. The latter need to be addressed with unsupervisedsolutions in order to make a system ecient forreal-time tasks and at the same time exible in order toadopt it in any domain without requiring the trainingof sentiment models. Finally, the deployment of suchsystem into real-world scenarios needs the developmentof usable solutions for accessing and analyzing data.Methods This paper presents the ReUS platform: a systemintegrating an unsupervised approach, based onopen information extraction strategies, for performingreal-time aspect-based sentiment analysis together withfacilities supporting decision makers in the analysis andvisualization of collected data.Results The ReUS platform has been validated from aquantitative and qualitative perspectives. First, the aspectextraction and polarity inference capabilities have been evaluated on three dataset used in likewise editionsof SemEval. Second, a user group has been invitedto judge the usability of the platform.Conclusion The developed platform demonstrated to besuitable for being used into real-world scenarios requiring(i) the capability of processing real-time opinionbaseddocuments streams and (ii) the availability ofusable facilities for analyzing and visualizing collecteddata. Examples of possible analysis and visualizationsincludes the presentation of lists ranking aspects bytheir importance of by their polarity values computedwithin the whole data repository. This kind of analysisenables, for instance, the discovery of product issues.
2018

Geiger Bernhard

The Global Benefits of Open Research and How It Can Change Scientific Publishing

Martyn Rittman, MDPI, 2018

Buch
This entry for the 2018 MDPI English Writing Prize has been published as a chapter of "The Global Benefits of Open Research", edited by Martyn Rittman.
2018

Ross-Hellauer Anthony, Schmidt Birgit, Kramer Bianca

Are Funder Open Access Platforms a Good Idea?

Sage Open, 2018

Journal
As open access (OA) to publications continues to gather momentum, we should continuously question whether it is moving in the right direction. A novel intervention in this space is the creation of OA publishing platforms commissioned by funding organizations. Examples include those of the Wellcome Trust and the Gates Foundation, as well as recently announced initiatives from public funders like the European Commission and the Irish Health Research Board. As the number of such platforms increases, it becomes urgently necessary to assess in which ways, for better or worse, this emergent phenomenon complements or disrupts the scholarly communications landscape. This article examines ethical, organizational, and economic strengths and weaknesses of such platforms, as well as usage and uptake to date, to scope the opportunities and threats presented by funder OA platforms in the ongoing transition to OA. The article is broadly supportive of the aims and current implementations of such platforms, finding them a novel intervention which stands to help increase OA uptake, control costs of OA, lower administrative burden on researchers, and demonstrate funders’ commitment to fostering open practices. However, the article identifies key areas of concern about the potential for unintended consequences, including the appearance of conflicts of interest, difficulties of scale, potential lock-in, and issues of the branding of research. The article ends with key recommendations for future consideration which include a focus on open scholarly infrastructure.
2018

Schmidt Birgit, Ross-Hellauer Anthony, van Edig Xenia, Moylan Elizabeth

Ten considerations for open peer review [version 1; referees: 2 approved]

F1000Research, 2018

Journal
Open peer review (OPR), as with other elements of open science and open research, is on the rise. It aims to bring greater transparency and participation to formal and informal peer review processes. But what is meant by `open peer review', and what advantages and disadvantages does it have over standard forms of review? How do authors or reviewers approach OPR? And what pitfalls and opportunities should you look out for? Here, we propose ten considerations for OPR, drawing on discussions with authors, reviewers, editors, publishers and librarians, and provide a pragmatic, hands-on introduction to these issues. We cover basic principles and summarise best practices, indicating how to use OPR to achieve best value and mutual benefits for all stakeholders and the wider research community.
2018

Egger Peter

Smart Contracts and the Blockchain-Revolution in the area of conflict between Technology and Law

Wirtschaftskammer Österreich, MANZ'sche Verlags- und Universitätsbuchhandlung GmbH, 2018

Buch
Similar to the creation of the internet, today the world is captivated by a new phenomenon that is considered to produce substantial impact on economic life. We are talking about the Blockchain technology, which was initially introduced as the technological backbone of the cryptocurrency Bitcoin, and is in a simplified form representing a decentralized data storage system within a peer-to-peer network. As Blockchains can be implemented in many areas of life, there are continuously emerging new ideas of utilization. One of the most promising areas of application are so-called “Smart Contracts”, which introduce new codified relationships that are both defined and automatically enforced by a code. However, fully organizing legal relationships by Smart Contracts in the near future also raises doubt on the consistency of connected processes with the law. This article examines Smart Contracts from a legal perspective, specifically explaining its place in existing Austrian contract law.
2018

Geiger Bernhard

A Short Note on the Jensen-Shannon Divergence between Simple Mixture Distributions

2018

Buch
This short note presents results about the symmetric Jensen-Shannon divergence between two discrete mixture distributions p1 and p2. Specifically, for i=1,2, pi is the mixture of a common distribution q and a distribution p̃ i with mixture proportion λi. In general, p̃ 1≠p̃ 2 and λ1≠λ2. We provide experimental and theoretical insight to the behavior of the symmetric Jensen-Shannon divergence between p1 and p2 as the mixture proportions or the divergence between p̃ 1 and p̃ 2 change. We also provide insight into scenarios where the supports of the distributions p̃ 1, p̃ 2, and q do not coincide.
2018

Luzhnica Granit, Veas Eduardo Enrique

Skin Reading Meets Speech Recognition and Object Recognition for Sensory Substitution

ACM Ubicomp, ACM, 2018

Konferenz
Sensory substitution has been a research subject for decades, and yet its applicability outside of the research is very limited. Thus creating scepticism among researchers that a full sensory substitution is not even possible [8]. In this paper, we do not substitute the entire perceptual channel. Instead, we follow a different approach which reduces the captured information drastically. We present concepts and implementation of two mobile applications which capture the user's environment, describe it in the form of text and then convey its textual description to the user through a vibrotactile wearable display. The applications target users with hearing and vision impairments.
2018

Yenikent Seren, Holtz Peter, Thalmann_TU Stefan, Mathieu d'Aquin, Joachim Kimmerle

Evaluating the AFEL Learning Tool: Didactalia Users’ Experiences with Personalized Recommendations and Interactive Visualizations

CEUR Workshop Proceedings, Angela Fessl, Stefan Thalmann, Mathieu d'Aquin, Peter Holtz, Stefan Dietze, 2018

Learning technologies offer opportunities for users to enhance and to personalize selfregulatedlearning activities. In this paper, we present the results of a laboratory study that coversthe user evaluation of the AFEL Didactalia app, which analyzes everyday learning activities oflearners, extracts learning scopes and trajectories, and provides personalized recommendationsof learning resources as well as an interactive visualization of learning activities. Participants ofthe study (N=76) engaged with the tool after first completing an assigned learning task related togeography or history using learning materials from the Didactalia platform for approximately 30minutes and afterwards freely exploring the platform for 30-45 minutes. The related behaviordata was tracked and analyzed by the AFEL learning tool. After completing the two tasks,participants received an introduction to the tool and explored them as well. The results suggest asatisfactory experience with the tool and provide insights on the potential benefits, as well asaspects to be improved for further development. We discuss our findings and the next steps ofthe investigation in detail.
2018

Duricic Tomislav, Lacic Emanuel, Kowald Dominik, Lex Elisabeth

Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalenc

RecSys 2018, ACM, Vancouver, Canada, 2018

Konferenz
User-based Collaborative Filtering (CF) is one of the most popularapproaches to create recommender systems. Œis approach is basedon €nding the most relevant k users from whose rating history wecan extract items to recommend. CF, however, su‚ers from datasparsity and the cold-start problem since users o‰en rate only asmall fraction of available items. One solution is to incorporateadditional information into the recommendation process such asexplicit trust scores that are assigned by users to others or implicittrust relationships that result from social connections betweenusers. Such relationships typically form a very sparse trust network,which can be utilized to generate recommendations for users basedon people they trust. In our work, we explore the use of a measurefrom network science, i.e. regular equivalence, applied to a trustnetwork to generate a similarity matrix that is used to select thek-nearest neighbors for recommending items. We evaluate ourapproach on Epinions and we €nd that we can outperform relatedmethods for tackling cold-start users in terms of recommendationaccuracy
2018

Gursch Heimo, Silva Nelson, Reiterer Bernhard , Paletta Lucas , Bernauer Patrick, Fuchs Martin, Veas Eduardo Enrique, Kern Roman

Flexible Scheduling for Human Robot Collaboration in Intralogistics Teams

Mensch und Computer 2018, Gesellschaft für Informatik e.V., Gesellschaft für Informatik e.V., Bonn, Germany, 2018

Konferenz
The project Flexible Intralogistics for Future Factories (FlexIFF) investigates human-robot collaboration in intralogistics teams in the manufacturing industry, which form a cyber-physical system consisting of human workers, mobile manipulators, manufacturing machinery, and manufacturing information systems. The workers use Virtual Reality (VR) and Augmented Reality (AR) devices to interact with the robots and machinery. The right information at the right time is key for making this collaboration successful. Hence, task scheduling for mobile manipulators and human workers must be closely linked with the enterprise’s information systems, offering all actors on the shop floor a common view of the current manufacturing status. FlexIFF will provide useful, well-tested, and sophisticated solutions for cyberphysicals systems in intralogistics, with humans and robots making the most of their strengths, working collaboratively and helping each other.
2018

Kowald Dominik, Lex Elisabeth

Studying Confirmation Bias in Hashtag Usage on Twitte

European Computation Social Sciences Symposium, Cologne, Germany, 2018

Konferenz
The micro-blogging platform Twitter allows its nearly 320 million monthly active users to build a network of follower connections to other Twitter users (i.e., followees) in order to subscribe to content posted by these users. With this feature, Twitter has become one of the most popular social networks on the Web and was also the first platform that offered the concept of hashtags. Hashtags are freely-chosen keywords, which start with the hash character, to annotate, categorize and contextualize Twitter posts (i.e., tweets).Although hashtags are widely accepted and used by the Twitter community, the heavy reuse of hashtags that are popular in the personal Twitter networks (i.e., own hashtags and hashtags used by followees) can lead to filter bubble effects and thus, to situations, in which only content associated with these hashtags are presented to the user. These filter bubble effects are also highly associated with the concept of confirmation bias, which is the tendency to favor and reuse information that confirms personal preferences. One example would be a Twitter user who is interested in political tweets of US president Donald Trump. Depending on the hashtags used, the user could either be stuck in a pro-Trump (e.g., #MAGA) or contra-Trump (e.g., #fakepresident) filter bubble. Therefore, the goal of this paper is to study confirmation bias and filter bubble effects in hashtag usage on Twitter by treating the reuse of hashtags as a phenomenon that fosters confirmation bias.
2018

Dennerlein Sebastian, Kowald Dominik, Lex Elisabeth, Ley Tobias, Pammer-Schindler Viktoria

Simulation-based Co-Creation of Algorithm

Workshop on Co-Creation in the Design, Development and Implementation of Technology-Enhanced Learning (CCTEL'2018, Springer, Leeds, England, 2018

Konferenz
Co-Creation methods for interactive computer systems design are by now widely accepted as part of the methodological repertoire in any software development process. As the communityis becoming more and more aware of the factthat software is driven by complex, artificially intelligent algorithms, the question arises what “co-creation of algorithms” in the sense of users ex-plicitly shaping the parameters of algorithms during co-creation, could mean, and how it would work. They are not tangible like featuresin a tool and desired effects are harder to be explained or understood. Therefore, we propose an it-erative simulation-based Co-Design approach that allows to Co-Create Algo-rithms together with the domain professionals by making their assumptions and effects observable. The proposal is a methodological idea for discussion within the EC-TEL community, yet to be applied in a research practice
2018

Dennerlein Sebastian, Mayr Melanie

med360 - Poster @ MEI 2018

2018

Konferenz
The number of scientific publications has rapidly increased over the last decades and still shows asteady growth. In addition, medical scientists and practitioners often have to deal with multiplesystems and databases for literature search. This results in information overload and professionalshardly being able to keep up to date with the latest scientific publications in their limited free time.We, therefore, founded a design team called ‘med360’ and developed a taylor-made web tool toprovide proactively literature in accordance to the interests of the user. Approximately five to eightpersons have been involved in the collaborative design of the med360-tool over a period of sixmonths from paper to software prototyping. For this purpose, workshops and interviews wereconducted with relevant stakeholders such as the domain professionals, developers and researchers.Instead of crawling through multiple systems like research gate, google scholar or publisher websites,our study indicates, that HC professionals require an easy-to-use tool. It must be in line with theirbusy work life and give access to literature in one place in reasonable extent. In consequence,med360 allows for a straight forward definition of the search scope by entering a few keywords andproviding a forecast of the expected number of papers per week. The identified literature ispresented in the well-adopted mailbox format on mobiles,tablets and personal computers frompredefined literature systems and databases.This way, med360 helps researcher to better cope with their workload: “With med360, I feel like Ican survive my work day”.
2018

Cuder Gerald, Baumgartner Christian

A data mining strategy for the search and classification of gene expression data in cancer

ÖGBMT - Jahrestagung 201, ÖGBMT - Österreichische Gesellschaft für Biomedizinische Techni, Hall in Tirol, 2018

Konferenz
Cancer is one of the most uprising diseases in our modern society and is defined by an uncontrolled growth of tissue. This growth is caused by mutation on the cellular level. In this thesis, a data-mining workflow was developed to find these responsible genes among thousands of irrelevant ones in three microarray datasets of different cancer types by applying machine learning methods such as classification and gene selection. In this work, four state-of-the-art selection algorithms are compared with a more sophisticated method, termed Stacked-Feature Ranking (SFR), further increasing the discriminatory ability in gene selection.
2018

Lacic Emanuel, Kowald Dominik, Lex Elisabeth

Neighborhood Troubles: On the Value of User Pre-Filtering ToSpeed Up and Enhance Recommendation

CIKM 2018 Workshop Proceedings, Turin, Italy, 2018

Konferenz
In this paper, we present work-in-progress on applying user pre-filtering to speed up and enhance recommendations based on Collab-orative Filtering. We propose to pre-filter users in order to extracta smaller set of candidate neighbors, who exhibit a high numberof overlapping entities and to compute the final user similaritiesbased on this set. To realize this, we exploit features of the high-performance search engine Apache Solr and integrate them into ascalable recommender system. We have evaluated our approachon a dataset gathered from Foursquare and our evaluation resultssuggest that our proposed user pre-filtering step can help to achieveboth a better runtime performance as well as an increase in overallrecommendation accuracy
2018

Kowald Dominik, Lacic Emanuel, Theiler Dieter, Lex Elisabeth

AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments.

CIKM 2018 Workshop Proceedings, CEUR, Turin, Italy, 2018

Konferenz
In this paper, we present preliminary results of AFEL-REC, a rec-ommender system for social learning environments. AFEL-RECis build upon a scalable so‰ware architecture to provide recom-mendations of learning resources in near real-time. Furthermore,AFEL-REC can cope with any kind of data that is present in sociallearning environments such as resource metadata, user interactionsor social tags. We provide a preliminary evaluation of three rec-ommendation use cases implemented in AFEL-REC and we €ndthat utilizing social data in form of tags is helpful for not only im-proving recommendation accuracy but also coverage. ‘is papershould be valuable for both researchers and practitioners inter-ested in providing resource recommendations in social learningenvironments
2018

Lovric Mario, Banic Ivana, Cuder Gerald, Kern Roman, Turkalj Mirjana

Phenotype-driven prediction of treatment outcomes in pediatric asthma patient

Current opinion in Allergy and Clinical Immunolog, Elsevier, 2018

Journal
2018

Silva Nelson, Schreck Tobias, Veas Eduardo Enrique, Sabol Vedran, Eggeling Eva, Fellner Dieter W.

Leveraging Eye-gaze and Time-series Features to Predict User Interests and Build a Recommendation Model for Visual Analysis

ACM Symposium on Eye Tracking Research and Applications ETRA, ACM, 2018

Konferenz
We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.
2018

Cicchinelli Analia, Veas Eduardo Enrique, Pardo Abelardo, Pammer-Schindler Viktoria, Fessl Angela, Barreiros Carla, Lindstaedt Stefanie

Finding traces of self-regulated learning in activity streams

ACM Conference on Learning Analytics and Knowledge, LAK , ACM, ACM, 2018

Konferenz
This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.
2017

Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph

Tags, Titles or Q & A: Choosing Content Descriptors for Visual Recommender Systems

Proceedings of the 28th ACM Conference on Hypertext and Social Media, ACM, Prague, Czech Republic, 2017

Konferenz
In today's digital age with an increasing number of websites, social/learning platforms, and different computer-mediated communication systems, finding valuable information is a challenging and tedious task, regardless from which discipline a person is. However, visualizations have shown to be effective in dealing with huge datasets: because they are grounded on visual cognition, people understand them and can naturally perform visual operations such as clustering, filtering and comparing quantities. But, creating appropriate visual representations of data is also challenging: it requires domain knowledge, understanding of the data, and knowledge about task and user preferences. To tackle this issue, we have developed a recommender system that generates visualizations based on (i) a set of visual cognition rules/guidelines, and (ii) filters a subset considering user preferences. A user places interests on several aspects of a visualization, the task or problem it helps to solve, the operations it permits, or the features of the dataset it represents. This paper concentrates on characterizing user preferences, in particular: i) the sources of information used to describe the visualizations, the content descriptors respectively, and ii) the methods to produce the most suitable recommendations thereby. We consider three sources corresponding to different aspects of interest: a title that describes the chart, a question that can be answered with the chart (and the answer), and a collection of tags describing features of the chart. We investigate user-provided input based on these sources collected with a crowd-sourced study. Firstly, information-theoretic measures are applied to each source to determine the efficiency of the input in describing user preferences and visualization contents (user and item models). Secondly, the practicability of each input is evaluated with content-based recommender system. The overall methodology and results contribute methods for design and analysis of visual recommender systems. The findings in this paper highlight the inputs which can (i) effectively encode the content of the visualizations and user's visual preferences/interest, and (ii) are more valuable for recommending personalized visualizations.
2017

Kaiser René, Meixner Britta, Jäger Joscha

Reflecting on the Workshop on Interactive Content Consumption (WSICC) Series

IEEE MultiMedia Magazine, IEEE Computer Society, IEEE, 2017

Journal
Enabling interactive access to multimedia content and evaluating content-consumption behaviors and experiences involve several different research areas, which are covered at many different conferences. For four years, the Workshop on Interactive Content Consumption (WSICC) series offered a forum for combining interdisciplinary, comprehensive views, inspiring new discussions related to interactive multimedia. Here, the authors reflect on the outcome of the series.
2017

Gursch Heimo, Cemernek David, Kern Roman

Multi-Loop Feedback Hierarchy Involving Human Workers in Manufacturing Processes

Mensch und Computer 2017 - Workshopband, Manuel Burghardt, Raphael Wimmer, Christian Wolff, Christa Womser-Hacker, Gesellschaft für Informatik e.V., Regensburg, 2017

Konferenz
In manufacturing environments today, automated machinery works alongside human workers. In many cases computers and humans oversee different aspects of the same manufacturing steps, sub-processes, and processes. This paper identifies and describes four feedback loops in manufacturing and organises them in terms of their time horizon and degree of automation versus human involvement. The data flow in the feedback loops is further characterised by features commonly associated with Big Data. Velocity, volume, variety, and veracity are used to establish, describe and compare differences in the data flows.
2017

Shao Lin, Silva Nelson, Schreck Tobias, Eggeling Eva

Visual Exploration of Large Scatter Plot Matrices by Pattern Recommendation based on Eye Tracking

ESIDA 2017 - Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics, co-located with IUI 2017 - International Conference on Intelligent User Interfaces, ACM, Limassol, Cyprus, 2017

Konferenz
The Scatter Plot Matrix (SPLOM) is a well-known technique for visual analysis of high-dimensional data. However, one problem of large SPLOMs is that typically not all views are potentially relevant to a given analysis task or user. The matrix itself may contain structured patterns across the dimensions, which could interfere with the investigation for unexplored views. We introduce a new concept and prototype implementation for an interactive recommender system supporting the exploration of large SPLOMs based on indirectly obtained user feedback from user eye tracking. Our system records the patterns that are currently under exploration based on gaze times, recommending areas of the SPLOM containing potentially new, unseen patterns for successive exploration. We use an image-based dissimilarity measure to recommend patterns that are visually dissimilar to previously seen ones, to guide the exploration in large SPLOMs. The dynamic exploration process is visualized by an analysis provenance heatmap, which captures the duration on explored and recommended SPLOM areas. We demonstrate our exploration process by a user experiment, showing the indirectly controlled recommender system achieves higher pattern recall as compared to fully interactive navigation using mouse operations.
2017

Cemernek David, Gursch Heimo, Kern Roman

Big Data as a Promoter of Industry 4.0: Lessons of the Semiconductor Industry

IEEE 15th International Conference of Industrial Informatics - INDIN'2017, IEEE, Emden, Germany, 2017

Konferenz
The catchphrase “Industry 4.0” is widely regarded as a methodology for succeeding in modern manufacturing. This paper provides an overview of the history, technologies and concepts of Industry 4.0. One of the biggest challenges to implementing the Industry 4.0 paradigms in manufacturing are the heterogeneity of system landscapes and integrating data from various sources, such as different suppliers and different data formats. These issues have been addressed in the semiconductor industry since the early 1980s and some solutions have become well-established standards. Hence, the semiconductor industry can provide guidelines for a transition towards Industry 4.0 in other manufacturing domains. In this work, the methodologies of Industry 4.0, cyber-physical systems and Big data processes are discussed. Based on a thorough literature review and experiences from the semiconductor industry, we offer implementation recommendations for Industry 4.0 using the manufacturing process of an electronics manufacturer as an example.
2017

Meixner Britta, Kaiser René, Jäger Joscha, Ooi Wei Tsang, Kosch Harald

"INTERACTIVE MEDIA: TECHNOLOGY AND EXPERIENCE" Springer Multimedia Tools and Applications (MTAP) Journal

Springer Multimedia Tools and Applications (MTAP), Springer, Springer US, 2017

Buch
(journal special issue)
2017

Barreiros Carla, Veas Eduardo Enrique, Pammer-Schindler Viktoria

BioIoT: Communicating Sensory Information of a Coffee Machine Using a Nature Metaphor

CHI '17 CHI Conference on Human Factors in Computing Systems, ACM, ACM, Denver, Colorado, USA, 2017

Konferenz
In our research we explore representing the state of production machines using a new nature metaphor, called BioIoT. The underlying rationale is to represent relevant information in an agreeable manner and to increase machines’ appeal to operators. In this paper we describe a study with twelve participants in which sensory information of a coffee machine is encoded in a virtual tree. All participants considered the interaction with the BioIoT pleasant; and most reported to feel more inclined to perform machine maintenance, take “care” for the machine, than given classic state representation. The study highlights as directions for follow-up research personalization, intelligibility vs representational power, limits of the metaphor, and immersive visualization.
2017

Trattner Christoph, Elsweiler David

Investigating the Healthiness of Internet-Sourced Recipes

Proceedings of the 26th International Conference on World Wide Web, ACM, Perth, Australia, 2017

Konferenz
Food recommenders have the potential to positively in uence theeating habits of users. To achieve this, however, we need to understandhow healthy recommendations are and the factors whichin uence this. Focusing on two approaches from the literature(single item and daily meal plan recommendation) and utilizing alarge Internet sourced dataset from Allrecipes.com, we show howalgorithmic solutions relate to the healthiness of the underlyingrecipe collection. First, we analyze the healthiness of Allrecipes.comrecipes using nutritional standards from the World Health Organisationand the United Kingdom Food Standards Agency. Second,we investigate user interaction patterns and how these relate to thehealthiness of recipes. Third, we experiment with both recommendationapproaches. Our results indicate that overall the recipes inthe collection are quite unhealthy, but this varies across categorieson the website. Users in general tend to interact most often with theleast healthy recipes. Recommender algorithms tend to score popularitems highly and thus on average promote unhealthy items. Thiscan be tempered, however, with simple post- ltering approaches,which we show by experiment are better suited to some algorithmsthan others. Similarly, we show that the generation of meal planscan dramatically increase the number of healthy options open tousers. One of the main ndings is, nevertheless, that the utilityof both approaches is strongly restricted by the recipe collection.Based on our ndings we draw conclusions how researchers shouldattempt to make food recommendation systems promote healthynutrition.
2017

Seitlinger Paul, Ley Tobias, Kowald Dominik, Theiler Dieter, Hasani-Mavriqi Ilire, Dennerlein Sebastian, Lex Elisabeth, Albert D.

Balancing the Fluency-Consistency Tradeoff in Collaborative Information Search Using a Recommender Approach

International Journal of Human-Computer Interaction, Constantine Stephanidis and Gavriel Salvendy , Taylor and Francis, 2017

Journal
Creative group work can be supported by collaborative search and annotation of Web resources. In this setting, it is important to help individuals both stay fluent in generating ideas of what to search next (i.e., maintain ideational fluency) and stay consistent in annotating resources (i.e., maintain organization). Based on a model of human memory, we hypothesize that sharing search results with other users, such as through bookmarks and social tags, prompts search processes in memory, which increase ideational fluency, but decrease the consistency of annotations, e.g., the reuse of tags for topically similar resources. To balance this tradeoff, we suggest the tag recommender SoMe, which is designed to simulate search of memory from user-specific tag-topic associations. An experimental field study (N = 18) in a workplace context finds evidence of the expected tradeoff and an advantage of SoMe over a conventional recommender in the collaborative setting. We conclude that sharing search results supports group creativity by increasing the ideational fluency, and that SoMe helps balancing the evidenced fluency-consistency tradeoff.
2017

Barreiros Carla, Veas Eduardo Enrique, Pammer-Schindler Viktoria

Can a green thumb make the difference? Using a Nature Metaphor to Communicate Sensor Information of a Coffee Machine

IEEE Consumers Electronics Magazine, 2017

Journal
This paper describes a novel visual metaphor to communicate sensor information of a connected device. The Internet of Things aims to extend every device with sensing and computing capabilities. A byproduct is that even domestic machines become increasingly complex, tedious to understand and maintain. This paper presents a prototype instrumenting a coffee machine with sensors. The machine streams the sensor data, which is picked up by an augmented reality application serving a nature metaphor. The nature metaphor, BioAR, represents the status derived from the coffee machine sensors in the features of a 3D virtual tree. The tree is meant to pass for a living proxy of the machine it represents. The metaphor, shown either with AR or a simple holographic display, reacts to the user manipulation of the machine and its workings. A first user study validates that the representation is correctly understood, and that it inspires affect for the machine. A second user study validates that the metaphor scales to a large number of machines.
2017

Reiter-Haas Markus, Slawicek Valentin, Lacic Emanuel

Studo Jobs: Enriching Data With Predicted Job Labels

ACM, Graz, 2017

Konferenz
2017

Hasitschka Peter, Sabol Vedran, Thalmann Stefan

Toward a Visual Analytics Framework for Learning Communities in Industry 4.0

9. Konferenz Professionelles Wissensmanagement (Professional Knowledge Management), York Sure-Vetter, Stefan Zander, Andreas Harth, Karlsruhe, Deutschland, 2017

Konferenz
Industry 4.0 describes the digitization and the interlinkingof companies working together in a supply chain [1]. Thereby,the digitization and the interlinking does not only affects themachines and IT infrastructure, rather also the employees areaffected [3]. The employees have to acquire more and morecomplex knowledge within a shorter period of time. To copewith this challenge, the learning needs to be integrated into thedaily work practices, while the learning communities shouldmap the organizational production networks [2]. Such learningnetworks support the knowledge exchange and joint problemsolving together with all involved parties [4]. However, insuch communities not all involved actors are known and hencesupport to find the right learning material and peers is needed.Nowadays, many different learning environments are usedin the industry. Their complexity makes it hard to understandwhether the system provides an optimal learning environment.The large number of learning resources, learners and theiractivities makes it hard to identify potential problems inside alearning environment. Since the human visual system providesenormous power for discovering patterns from data displayedusing a suitable visual representation [5], visualizing such alearning environment could provide deeper insights into itsstructure and activities of the learners.Our goal is to provide a visual framework supporting theanalysis of communities that arise in a learning environment.Such analysis may lead to discovery of information that helpsto improve the learning environment and the users’ learningsuccess.
2017

Dennerlein Sebastian, Ginthör Robert, Breitfuß Gert, Pammer-Schindler Viktoria, Stern Hermann

Bringing Big Data to Adolescence - Specifying Business Models by Practice

Institute of Systems Sciences, Innovation and Sustainability Research , Merangasse 18 /I , A - 8010 Graz, Austria, Baumgartner, R.J., Fuellsack, M., Gelbmann, U., Rauter, R, Graz, Austria, 2017

Konferenz
To specify the current understanding of business models in the realm of Big Data, we used a qualitative approach analysing 25 Big Data projects spread over the domains of Retail, Energy, Production, and Life Sciences, and various company types (SME, group, start-up, etc.). All projects have been conducted in the last two years at Austria’s competence center for Data-driven Business and Big Data Analytics, the Know-Center.
2017

Toller Maximilian, Kern Roman

Robust Parameter-Free Season Length Detection in Time Series

MILETS 2017 @ International Conference on Knowledge Discovery and Data Mining, Halfiax, Nova Scotia Canada, 2017

Konferenz
The in-depth analysis of time series has gained a lot of re-search interest in recent years, with the identification of pe-riodic patterns being one important aspect. Many of themethods for identifying periodic patterns require time series’season length as input parameter. There exist only a few al-gorithms for automatic season length approximation. Manyof these rely on simplifications such as data discretization.This paper presents an algorithm for season length detec-tion that is designed to be sufficiently reliable to be used inpractical applications. The algorithm estimates a time series’season length by interpolating, filtering and detrending thedata. This is followed by analyzing the distances betweenzeros in the directly corresponding autocorrelation function.Our algorithm was tested against a comparable algorithmand outperformed it by passing 122 out of 165 tests, whilethe existing algorithm passed 83 tests. The robustness of ourmethod can be jointly attributed to both the algorithmic ap-proach and also to design decisions taken at the implemen-tational level.
2017

Ziak Hermann, Kern Roman

Evaluation of Contextualization and Diversification Approaches in Aggregated Search

TIR @ DEXA International Conference on Database and Expert Systems Applications, 2017

Konferenz
The combination of different knowledge bases in thefield of information retrieval is called federated or aggregated search. It has several benefits over single source retrieval but poses some challenges as well. This work focuses on the challenge of result aggregation; especially in a setting where the final result list should include a certain degree of diversity and serendipity. Both concepts have been shown to have an impact on how user perceive an information retrieval system. In particular, we want to assess if common procedures for result list aggregation can be utilized to introduce diversity and serendipity. Furthermore, we study whether a blocking or interleaving for result aggregation yields better results. In a cross vertical aggregated search the so-called verticalscould be news, multimedia content or text. Block ranking is one approach to combine such heterogeneous result. It relies on the idea that these verticals are combined into a single result list as blocks of several adjacent items. An alternative approach for this is interleaving. Here the verticals are blended into one result list on an item by item basis, i.e. adjacent items in the result list may come from different verticals. To generate the diverse and serendipitous results we reliedon a query reformulation technique which we showed to be beneficial to generate diversified results in previous work. To conduct this evaluation we created a dedicated dataset. This dataset served as a basis for three different evaluation settings on a crowd sourcing platform, with over 300 participants. Our results show that query based diversification can be adapted to generate serendipitous results in a similar manner. Further, we discovered that both approaches, interleaving and block ranking, appear to be beneficial to introduce diversity and serendipity. Though it seems that queries either benefit from one approach or the other but not from both.
2017

Ruiz-Calleja Adolfo, Prieto Luis Pablo, Jesús Rodríguez Triana María , Dennerlein Sebastian, Ley Tobias

Learning Analytics for Professional and Workplace Learning: A Literature Review

EC-TEL 2017 EUROPEAN CONFERENCE ON TECHNOLOGY ENHANCED LEARNING, Springer, Tallinn, 2017

Konferenz
Despite the ubiquity of learning in the everyday life of most workplaces, the learning analytics community only has paid attention to such settings very recently. One probable reason for this oversight is the fact that learning in the workplace is often informal, hard to grasp and not univocally defined. This paper summarizes the state of the art of Workplace Learning Analytics (WPLA), extracted from a systematic literature review of five academic databases as well as other known sources in the WPLA community. Our analysis of existing proposals discusses particularly on the role of different conceptions of learning and their influence on the LA proposals’ design and technology choices. We end the paper by discussing opportunities for future work in this emergent field.
2017

Thalmann Stefan, Pammer-Schindler Viktoria

Die Rolle des Mitarbeiters in der Smart Factory

Wissensmanagement, 2017

Journal
Aktuelle Untersuchungen zeigen einerseits auf, dass der Mensch weiterhin eine zentrale Rolle in der Industrie spielt. Andererseits ist aber auch klar, dass die Zahl der direkt in der Produktion beschäftigten Mitarbeter sinken wird. Die Veränderung wird dahin gehen, dass der Mensch weniger gleichförmige Prozese bearbeitet, stattdessen aber in der Lage sein muss, sich schnell ändernden Arbeitstätigkeiten azupassen und individualisierte Fertigungsprozesse zu steuern. Die Reduktion der Mitarbeiter hat jedoch auch eine Reduktion von Redunanzen zur Folge. Dies führt dazu, dass dem Einzelnen mehr Verantwortung übertragen wird. Als Folge haben Fehlentscheidungen eine görßere Tragweite und bedeuten somit auch ein höheres Risikio. Der Erfolg einer Industrie 4.0 Kampagne wird daher im Wesentlichen von den Anpassungsfähigkeiten der Mitarbeiter abhängen.
2017

Geiger Manfred, Waizenegger Lena, Treasure-Jones Tamsin, Sarigianni Christina, Maier Ronald, Thalmann Stefan, Remus Ulrich

NOT JUST ANOTHER TYPE OF RESISTANCE–TOWARDS A DEEPER UNDERSTANDING OF SUPPORTIVE NON-USE

25th European Conference on Information Systems, AIS, Guimarães, Portugal, 2017

Konferenz
Research on information system (IS) adoption and resistance has accumulatedsubstantial theoretical and managerial knowledge. Surprisingly, the paradox that end userssupport and at the same time resist use of an IS has received relatively little attention. Theinvestigation of this puzzle, however, is important to complement our understanding ofresistant behaviours and consequently to strengthen the explanatory power of extanttheoretical constructs on IS resistance. We investigate an IS project within the healthcare ...
2017

Kraker Peter, Enkhbayar Asuraa, Schramm Maxi, Kittel Christopher, Chamberlain Scott, Skaug Mike , Brembs Björn

Open Knowledge Maps: A Visual Interface to the World’s Scientific Knowledge

Open Science Conference 2017, Berlin, 2017

Konferenz
2017

Ginthör Robert, Lamb Reinhold, Koinegg Johann

Green Big Data - der Rohstoff Daten in der Energie- und Abfallwirtschaft

Green Tech Cluster GmbH, 2017

Buch
Daten stellen den Rohstoff und die Basis für viele Unternehmen und deren künftigen wirtschaftlichen Erfolg in der Industrie dar. Diese Radar-Ausgabe knüpft inhaltlich an die veröffentlichten Radar-Ausgaben „Dienstleistungsinnovationen“ und „Digitalisierte Maschinen und Anlagen“ an und beleuchtet die technischen Möglichkeiten und zukünftigen Entwicklungen von Data-driven Business im Kontext der Green Tech Industries. Basierend auf der fortschreitenden Digitalisierung nimmt das Angebotan strukturierten und unstrukturierten Daten in den unterschiedlichen Bereichen der Wirtschaft rasant zu. In diesem Kontext gilt es sowohl interne als auch externe Daten unterschiedlichen Ursprungs zentral zu erfassen, zu validieren, miteinander zu kombinieren, auszuwerten sowie daraus neue Erkenntnisse und Anwendungen für ein Data DrivenBusiness zu generieren.
2017

Wilsdon James , Bar-Ilan Judit, Frodemann Robert, Lex Elisabeth, Peters Isabella , Wouters Paul

Next-generation altmetrics: responsible metrics and evaluation for open science

European Union, 2017

Journal
2017

Lacic Emanuel, Kowald Dominik, Lex Elisabeth

Tailoring Recommendations for a Multi-Domain Environment

ACM International Conference on Recommender Systems 2017, RecSys'2017, ACM, Como, Italy, 2017

Konferenz
Recommender systems are acknowledged as an essential instrumentto support users in finding relevant information. However,the adaptation of recommender systems to multiple domain-specificrequirements and data models still remains an open challenge. Inthe present paper, we contribute to this sparse line of research withguidance on how to design a customizable recommender systemthat accounts for multiple domains with heterogeneous data. Usingconcrete showcase examples, we demonstrate how to setup amulti-domain system on the item and system level, and we reportevaluation results for the domains of (i) LastFM, (ii) FourSquare,and (iii) MovieLens. We believe that our findings and guidelinescan support developers and researchers of recommender systemsto easily adapt and deploy a recommender system in distributedenvironments, as well as to develop and evaluate algorithms suitedfor multi-domain settings
2017

Pammer-Schindler Viktoria, Fessl Angela, Wiese Michael, Thalmann Stefan

Improving Search Strategies of Auditors – A Focus Group on Reflection Interventions

ARTEL Workshop at EC-TEL 2017, Tallin, Estland, 2017

Konferenz
Financial auditors routinely search internal as well as public knowledge bases as part of the auditing process. Efficient search strategies are crucial for knowledge workers in general and for auditors in particular. Modern search technology quickly evolves; and features beyond keyword search like fac-etted search or visual overview of knowledge bases like graph visualisations emerge. It is therefore desirable for auditors to learn about new innovations and to explore and experiment with such technologies. In this paper, we present a reflection intervention concept that intends to nudge auditors to reflect on their search behaviour and to trigger informal learning in terms of by trying out new or less frequently used search features. The reflection intervention concept has been tested in a focus group with six auditors using a mockup. Foremost, the discussion centred on the timing of reflection interventions and how to raise mo-tivation to achieve a change in search behaviour.
2017

de Reuver Mark, Tarkus Astrid, Haaker Timber, Breitfuß Gert, Roelfsema Melissa, Kosman Ruud, Heikkilä Marikka

Designing an ICT tooling platform to support the needs of SMEs in business model innovation

Unversity of Maribor, Faculty of Organizational Sciences, Andreja Pucihar, Ph.D., Mirjana Kljajić Borštnar, Ph.D., Christian Kittl, Ph.D., Pascal Ravesteijn, Ph.D., Roger Clarke, Ph.D., Roger Bons, Ph.D, University of Maribor Press, Maribor, 2017

Konferenz
In this paper, we present two design cycles for an online platform with ICT-enabled tooling that supports business model innovation by SMEs. The platform connects the needs of the SMEs regarding BMI with tools that can help to solve those needs and questions. The needs are derived from our earlier case study work (Heikkilä et al. 2016), showing typical BMI patterns of the SMEs needs - labelled as ‘I want to’s - about what an entrepreneur wants to achieve with business model innovation. The platform provides sets of integrated tools that can answer the typical ‘I want to’ questions that SMEs have with innovating their business models.
2017

Kowald Dominik, Kopeinik Simone , Lex Elisabeth

The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems

International Conference on User Modeling, Adaptation and Personalization 2017, UMAP'2017, ACM, Bratislava, 2017

Konferenz
Recommender systems have become important tools to supportusers in identifying relevant content in an overloaded informationspace. To ease the development of recommender systems, a numberof recommender frameworks have been proposed that serve a widerange of application domains. Our TagRec framework is one of thefew examples of an open-source framework tailored towards developingand evaluating tag-based recommender systems. In this paper,we present the current, updated state of TagRec, and we summarizeand reƒect on four use cases that have been implemented withTagRec: (i) tag recommendations, (ii) resource recommendations,(iii) recommendation evaluation, and (iv) hashtag recommendations.To date, TagRec served the development and/or evaluation processof tag-based recommender systems in two large scale Europeanresearch projects, which have been described in 17 research papers.‘us, we believe that this work is of interest for both researchersand practitioners of tag-based recommender systems.
2017

Görögh Edit, Vignoli Michela, Gauch Stephan, Blümel Clemens, Kraker Peter, Hasani-Mavriqi Ilire, Luzi Daniela , Walker Mappet, Toli Eleni, Sifacaki Electra

Opening up new channels for scholarly review, dissemination, and assessment

OpenSym 2017 International Symposium on Open Collaboration, Lorraine Morgan, ACM, Galway, Ireland, 2017

Konferenz
The growing dissatisfaction with the traditional scholarly communication process and publishing practices as well as increasing usage and acceptance of ICT and Web 2.0 technologies in research have resulted in the proliferation of alternative review, publishing and bibliometric methods. The EU-funded project OpenUP addresses key aspects and challenges of the currently transforming science landscape and aspires to come up with a cohesive framework for the review-disseminate-assess phases of the research life cycle that is fit to support and promote open science. The objective of this paper is to present first results and conclusions of the landscape scan and analysis of alternative peer review, altmetrics and innovative dissemination methods done during the first project year.
2017

Görögh Edit, Toli Eleni, Kraker Peter

Opening up the research lifecycle: review, assessment and dissemination of scholarly publications

Open Science Conference 2017, Berlin, 2017

Konferenz
2017

Thalmann Stefan, Thiele Janna, Manhart Markus, Virnes Marjo

Application Scenarios of Mobile Learning in Vocational Training: A Case Study of Ach So! in the Construction Sector

EdMedia: World Conference on Educational Media and Technology, Association for the Advancement of Computing in Education (AACE), Washington, DC, United States, 2017

Konferenz
This study explored the application scenarios of a mobile app called Ach So! forworkplace learning of construction work apprentices. The mobile application was used forpiloting new technology-enhanced learning practices in vocational apprenticeship trainingat construction sites in Finland and in a training center in Germany. Semi-structured focusgroup interviews were conducted after the pilot test periods. The interview data served asthe data source for the concept-driven framework analysis that employed theoretical ...
2017

Kopeinik Simone, Lex Elisabeth, Seitlinger Paul, Ley Tobias, Albert Dietrich

Supporting collaborative learning with tag recommendations: a real-world study in an inquiry-based classroom project

Proceedings of the 7th International Conference on Learning Analytics and Knowledge (LAK 2017), ACM, Vancouver, 2017

Konferenz
In online social learning environments, tagging has demonstratedits potential to facilitate search, to improve recommendationsand to foster reflection and learning.Studieshave shown that shared understanding needs to be establishedin the group as a prerequisite for learning. We hypothesisethat this can be fostered through tag recommendationstrategies that contribute to semantic stabilization.In this study, we investigate the application of two tag recommendersthat are inspired by models of human memory:(i) the base-level learning equation BLL and (ii) Minerva.BLL models the frequency and recency of tag use while Minervais based on frequency of tag use and semantic context.We test the impact of both tag recommenders on semanticstabilization in an online study with 56 students completinga group-based inquiry learning project in school. Wefind that displaying tags from other group members contributessignificantly to semantic stabilization in the group,as compared to a strategy where tags from the students’individual vocabularies are used. Testing for the accuracyof the different recommenders revealed that algorithms usingfrequency counts such as BLL performed better whenindividual tags were recommended. When group tags wererecommended, the Minerva algorithm performed better. Weconclude that tag recommenders, exposing learners to eachother’s tag choices by simulating search processes on learners’semantic memory structures, show potential to supportsemantic stabilization and thus, inquiry-based learning ingroups.
2017

Kowald Dominik, Pujari Suhbash Chandra, Lex Elisabeth

Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach

Proceedings of the 26th International World Wide Web Conference, WWW'2017, ACM, Perth, Western Australia, 2017

Konferenz
Hashtags have become a powerful tool in social platformssuch as Twitter to categorize and search for content, and tospread short messages across members of the social network.In this paper, we study temporal hashtag usage practices inTwitter with the aim of designing a cognitive-inspired hashtagrecommendation algorithm we call BLLI,S. Our mainidea is to incorporate the effect of time on (i) individualhashtag reuse (i.e., reusing own hashtags), and (ii) socialhashtag reuse (i.e., reusing hashtags, which has been previouslyused by a followee) into a predictive model. For this,we turn to the Base-Level Learning (BLL) equation from thecognitive architecture ACT-R, which accounts for the timedependentdecay of item exposure in human memory. Wevalidate BLLI,S using two crawled Twitter datasets in twoevaluation scenarios. Firstly, only temporal usage patternsof past hashtag assignments are utilized and secondly, thesepatterns are combined with a content-based analysis of thecurrent tweet. In both evaluation scenarios, we find not onlythat temporal effects play an important role for both individualand social hashtag reuse but also that our BLLI,S approachprovides significantly better prediction accuracy andranking results than current state-of-the-art hashtag recommendationmethods.
2017

Traub Matthias, Gursch Heimo, Lex Elisabeth, Kern Roman

Data Market Austria - Austria's First Digital Ecosystem for Data, Businesses, and Innovation

Exploring a changing view on organizing value creation: Developing New Business Models. Contributions to the 2nd International Conference on New Business Models, Institute of Systems Sciences, Innovation and Sustainability Research, Merangasse 18, 8010 Graz, Austria, Graz, 2017

Konferenz
New business opportunities in the digital economy are established when datasets describing a problem, data services solving the said problem, the required expertise and infrastructure come together. For most real-word problems finding the right data sources, services consulting expertise, and infrastructure is difficult, especially since the market players change often. The Data Market Austria (DMA) offers a platform to bring datasets, data services, consulting, and infrastructure offers to a common marketplace. The recommender systems included in DMA analyses all offerings, to derive suggestions for collaboration between them, like which dataset could be best processed by which data service. The suggestions should help the costumers on DMA to identify new collaborations reaching beyond traditional industry boundaries to get in touch with new clients or suppliers in the digital domain. Human brokers will work together with the recommender system to set up data value chains matching different offers to create a data value chain solving the problems in various domains. In its final expansion stage, DMA is intended to be a central hub for all actors participating in the Austrian data economy, regardless of their industrial and research domain to overcome traditional domain boundaries.
2017

Stabauer Petra, Breitfuß Gert, Lassnig Markus

Changing business models arising from digitalization. A best practice case study based on two Austrian companies

KF Uni Graz, Institut of System Sciences, Innovation and Sustainability, Graz, 2017

Konferenz
Nowadays digitalization is on everyone’s mind and affecting all areas of life. The rapid development of information technology and the increasing pervasiveness of digitalization represent new challenges to the business world. The emergence of the so-called fourth industrial revolution and the Internet of Things (IoT) confronts existing firms with changes in numerous aspects of doing business. Not only information and communication technologies are changing production processes through increasing automation. Digitalization can affect products and services itself. This could lead to major changes in a company’s value chain and as a consequence affects the company’s business model. In the age of digitalization, it is no longer sufficient to change single aspects of a firm’s business strategy, the business model itself needs to be the subject of innovation. This paper presents how digitalization affects business models of well-established companies in Austria. The results are demonstrated by means of two best practice case studies. The case studies were identified within an empirical research study funded by the Austrian Ministry for Transport, Innovation and Technology (BMVIT). The selected best practice cases presents how digitalization affects a firm’s business model and demonstrates the transformation of the value creation process by simultaneously contributing to sustainable development.
2017

Pammer-Schindler Viktoria, Fessl Angela, Weghofer Franz, Thalmann Stefan

Lernen 4.0 Herausforderungen für Menschen in der Industrie 4.0 erfolgreich meistern.

Productivity, 2017

Journal
Die Digitalisierung der Industrie wird aktuell sehr stark aus technoogischer Sicht betrachtet. Aber auch für den Menschen ergebn sich vielfältige Herausforderungen in dieser veränderten Arbeitsumgebung. Sie betreffen hautsächlich das Lernen von benötigtem Wissen.
2017

Thalmann Stefan, Larrazábal Jorge, Pammer-Schindler Viktoria, Kreuzthaler Armin, Fessl Angela

Fast Language Learning: Being Able to Manage Projects in a Foreign Language within Two Month

EdMedia: World Conference on Educational Media and Technology, Association for the Advancement of Computing in Education (AACE, Washington, DC, United States, 2017

Konferenz
n times of globalization, also workforce needs to be able to go global. This holds true especially for technical experts holding an exclusive expertise. Together with a global manufacturing company, we addressed the challenge of being able to send staff into foreign countries for managing technical projects in the foreign language. We developed a language learning concept that combines a language learning platform with conventional individual but virtually conducted coaching sessions. In our use case, we developed this ...
2017

Topps David, Dennerlein Sebastian, Treasure-Jones Tamsin

Raising the BarCamp: international reflections

MedEdPublish, 2017

Journal
There is increasing interest in Barcamps and Unconferences as an educational approach during traditional medical education conferences. Ourgroup has now accumulated extensive experience in these formats over a number of years in different educational venues. We present asummary of observations and lessons learned about what works and what doesn’t.
2017

Rexha Andi, Kröll Mark, Ziak Hermann, Kern Roman

Extending Scientific Literature Search by Including the Author’s Writing Style

Fifth Workshop on Bibliometric-enhanced Information Retrieval, Atanassova, I.; Bertin, M.; Mayr, P., Springer, Aberdeen, UK, 2017

Konferenz
Our work is motivated by the idea to extend the retrieval of related scientific literature to cases, where the relatedness also incorporates the writing style of individual scientific authors. Therefore we conducted a pilot study to answer the question whether humans can identity authorship once the topological clues have been removed. As first result, we found out that this task is challenging, even for humans. We also found some agreement between the annotators. To gain a better understanding how humans tackle such a problem, we conducted an exploratory data analysis. Here, we compared the decisions against a number of topological and stylometric features. The outcome of our work should help to improve automatic authorship identificationalgorithms and to shape potential follow-up studies.
2017

Pammer-Schindler Viktoria, Rivera-Pelayo Verónica, Fessl Angela, Müller Lars

Introducing Mood Self-Tracking at Work: Empirical Insights from Call Centers

ACM Transactions on Computer-Human Interaction (TOCHI), ACM New York, NY, USA , 2017

Journal
The benefits of self-tracking have been thoroughly investigated in private areas of life, like health or sustainable living, but less attention has been given to the impact and benefits of self-tracking in work-related settings. Through two field studies, we introduced and evaluated a mood self-tracking application in two call centers to investigate the role of mood self-tracking at work, as well as its impact on individuals and teams. Our studies indicate that mood self-tracking is accepted and can improve performance if the application is well integrated into the work processes and matches the management style. The results show that (i) capturing moods and explicitly relating them to work tasks facilitated reflection, (ii) mood self-tracking increased emotional awareness and this improved cohesion within teams, and (iii) proactive reactions by managers to trends and changes in team members’ mood were key for acceptance of reflection and correlated with measured improvements in work performance. These findings help to better understand the role and potential of self-tracking in work settings and further provide insights that guide future researchers and practitioners to design and introduce these tools in a workplace setting.
2017

Santos Tiago, Walk Simon, Helic Denis

Nonlinear Characterization of Activity Dynamics in Online Collaboration Websites

WWW '17 Companion Proceedings of the 26th International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee, Perth, Australia, 2017

Konferenz
Modeling activity in online collaboration websites, such asStackExchange Question and Answering portals, is becom-ing increasingly important, as the success of these websitescritically depends on the content contributed by its users. Inthis paper, we represent user activity as time series and per-form an initial analysis of these time series to obtain a bet-ter understanding of the underlying mechanisms that governtheir creation. In particular, we are interested in identifyinglatent nonlinear behavior in online user activity as opposedto a simpler linear operating mode. To that end, we applya set of statistical tests for nonlinearity as a means to char-acterize activity time series derived from 16 different onlinecollaboration websites. We validate our approach by com-paring activity forecast performance from linear and nonlin-ear models, and study the underlying dynamical systems wederive with nonlinear time series analysis. Our results showthat nonlinear characterizations of activity time series helpto (i) improve our understanding of activity dynamics in on-line collaboration websites, and (ii) increase the accuracy offorecasting experiments.
2017

Guerra Jorge, Catania Carlos, Veas Eduardo Enrique

Visual exploration of network hostile behavior

Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics, ACM, Limassol, Cyprus, 2017

Konferenz
This paper presents a graphical interface to identify hostilebehavior in network logs. The problem of identifying andlabeling hostile behavior is well known in the network securitycommunity. There is a lack of labeled datasets, which makeit difficult to deploy automated methods or to test the perfor-mance of manual ones. We describe the process of search-ing and identifying hostile behavior with a graphical tool de-rived from an open source Intrusion Prevention System, whichgraphically encodes features of network connections from alog-file. A design study with two network security expertsillustrates the workflow of searching for patterns descriptiveof unwanted behavior and labeling occurrences therewith.
2017

Rauter, R., Zimek, M.

Data Market Austria - Austria's First Digital Ecosystem for Data, Businesses, and Innovation

Exploring a changing view on organizing value creation: Developing New Business Models. Contributions to the 2nd International Conference on New Business Models, Institute of Systems Sciences, Innovation and Sustainability Research, Merangasse 18, 8010 Graz, Austria, Graz, 2017

Konferenz
New business opportunities in the digital economy are established when datasets describing a problem, data services solving the said problem, the required expertise and infrastructure come together. For most real-word problems finding the right data sources, services consulting expertise, and infrastructure is difficult, especially since the market players change often. The Data Market Austria (DMA) offers a platform to bring datasets, data services, consulting, and infrastructure offers to a common marketplace. The recommender systems included in DMA analyses all offerings, to derive suggestions for collaboration between them, like which dataset could be best processed by which data service. The suggestions should help the costumers on DMA to identify new collaborations reaching beyond traditional industry boundaries to get in touch with new clients or suppliers in the digital domain. Human brokers will work together with the recommender system to set up data value chains matching different offers to create a data value chain solving the problems in various domains. In its final expansion stage, DMA is intended to be a central hub for all actors participating in the Austrian data economy, regardless of their industrial and research domain to overcome traditional domain boundaries.
2017

Lukas Sabine, Pammer-Schindler Viktoria, Almer Alexander, Schnabel Thomas

Process optimization based on a multi-level management system for forest fire situations.

Workshop Data-Driven Decision Support in Digitized Work Environments - i-Know Workshop 2017, 2017

Konferenz
2017

Köfler Armin, Pammer-Schindler Viktoria, Almer Alexander, Schnabel Thomas

Supporting decision making in security forces’ command centre at large-scale events via videobased situation monitoring and base data supply - Extended Abstract

Workshop on Data-Driven Decision Support in Digitized Work Environments - i-Know Workshop 2017, Graz, 2017

Konferenz
2017

Rexha Andi, Kröll Mark, Ziak Hermann, Kern Roman

Pilot study: Ranking of textual snippets based on the writing style

Zenodo, 2017

In this pilot study, we tried to capture humans' behavior when identifying authorship of text snippets. At first, we selected textual snippets from the introduction of scientific articles written by single authors. Later, we presented to the evaluators a source and four target snippets, and then, ask them to rank the target snippets from the most to the least similar from the writing style.The dataset is composed by 66 experiments manually checked for not having any clear hint during the ranking for the evaluators. For each experiment, we have evaluations from three different evaluators.We present each experiment in a single line (in the CSV file), where, at first we present the metadata of the Source-Article (Journal, Title, Authorship, Snippet), and the metadata for the 4 target snippets (Journal, Title, Authorship, Snippet, Written From the same Author, Published in the same Journal) and the ranking given by each evaluator. This task was performed in the open source platform, Crowd Flower. The headers of the CSV are self-explained. In the TXT file, you can find a human-readable version of the experiment. For more information about the extraction of the data, please consider reading our paper: "Extending Scientific Literature Search by Including the Author’s Writing Style" @BIR: http://www.gesis.org/en/services/events/events-archive/conferences/ecir-workshops/ecir-workshop-2017
2017

Kowald Dominik

Modeling Activation Processes in Human Memory for Tag Recommendations: Using Models from Human Memory Theory to Implement Recommender Systems for Social Tagging and Microblogging Environment

Suedwestdeutscher Verlag für Hochschulschriften, TU Graz, Suedwestdeutscher Verlag für Hochschulschrifte, Graz, 2017

Buch
Social tagging systems enable users to collaboratively assign freely chosen keywords(i.e., tags) to resources (e.g., Web links). In order to support users in finding descrip-tive tags, tag recommendation algorithms have been proposed. One issue of currentstate-of-the-art tag recommendation algorithms is that they are often designed ina purely data-driven way and thus, lack a thorough understanding of the cognitiveprocesses that play a role when people assign tags to resources. A prominent exam-ple is the activation equation of the cognitive architecture ACT-R, which formalizesactivation processes in human memory to determine if a specific memory unit (e.g.,a word or tag) will be needed in a specific context. It is the aim of this thesis toinvestigate if a cognitive-inspired approach, which models activation processes inhuman memory, can improve tag recommendations.For this, the relation between activation processes in human memory and usagepractices of tags is studied, which reveals that (i) past usage frequency, (ii) recency,and (iii) semantic context cues are important factors when people reuse tags. Basedon this, a cognitive-inspired tag recommendation approach termed BLLAC+MPrisdeveloped based on the activation equation of ACT-R. An extensive evaluation usingsix real-world folksonomy datasets shows that BLLAC+MProutperforms currentstate-of-the-art tag recommendation algorithms with respect to various evaluationmetrics. Finally, BLLAC+MPris utilized for hashtag recommendations in Twitter todemonstrate its generalizability in related areas of tag-based recommender systems.The findings of this thesis demonstrate that activation processes in human memorycan be utilized to improve not only social tag recommendations but also hashtagrecommendations. This opens up a number of possible research strands for futurework, such as the design of cognitive-inspired resource recommender systems
2017

Breitfuß Gert, Kaiser René, Kern Roman, Kowald Dominik, Lex Elisabeth, Pammer-Schindler Viktoria, Veas Eduardo Enrique

i-Know Workshops 2017

CEUR Workshop Proceedings for i-know 2017 conference, CEUR , CEUR, Graz, Austria, 2017

Buch
Proceedings of the Workshop Papers of i-Know 2017, co-located with International Conference on Knowledge Technologies and Data-Driven Business 2017 (i-Know 2017), Graz, Austria, October 11-12, 2017.
2017

di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique

Supporting Exploratory Search with a Visual User-Driven Approach

Transactions on Interactive Intelligent Systems, ACM, 2017

Journal
Whenever users engage in gathering and organizing new information, searching and browsing activities emerge at the core of the exploration process. As the process unfolds and new knowledge is acquired, interest drifts occur inevitably and need to be accounted for. Despite the advances in retrieval and recommender algorithms, real-world interfaces have remained largely unchanged: results are delivered in a relevance-ranked list. However, it quickly becomes cumbersome to reorganize resources along new interests, as any new search brings new results. We introduce an interactive user-driven tool that aims at supporting users in understanding, refining, and reorganizing documents on the fly as information needs evolve. Decisions regarding visual and interactive design aspects are tightly grounded on a conceptual model for exploratory search. In other words, the different views in the user interface address stages of awareness, exploration, and explanation unfolding along the discovery process, supported by a set of text-mining methods. A formal evaluation showed that gathering items relevant to a particular topic of interest with our tool incurs in a lower cognitive load compared to a traditional ranked list. A second study reports on usage patterns and usability of the various interaction techniques within a free, unsupervised setting.
2017

d'Aquin Mathieu , Adamou Alessandro , Dietze Stefan , Fetahu Besnik , Gadiraju Ujwal , Hasani-Mavriqi Ilire, Holz Peter, Kümmerle Joachim, Kowald Dominik, Lex Elisabeth, Lopez Sola Susana, Mataran Ricardo, Sabol Vedran, Troullinou Pinelopi, Veas Eduardo, Veas Eduardo Enrique

AFEL: Towards Measuring Online Activities Contributions to Self-Directed Learning

7th Workshop on Awareness and Reflection in Technology Enhanced Learning (ARTEL 2017), Kravcik M., Mikroyannidis A., Pammer-Schindler V., Prilla M., CEUR-WS, Tallinn, Estonia, 2017

Konferenz
More and more learning activities take place online in a self-directed manner. Therefore, just as the idea of self-tracking activities for fitness purposes has gained momentum in the past few years, tools and methods for awareness and self-reflection on one's own online learning behavior appear as an emerging need for both formal and informal learners. Addressing this need is one of the key objectives of the AFEL (Analytics for Everyday Learning) project. In this paper, we discuss the different aspects of what needs to be put in place in order to enable awareness and self-reflection in online learning. We start by describing a scenario that guides the work done. We then investigate the theoretical, technical and support aspects that are required to enable this scenario, as well as the current state of the research in each aspect within the AFEL project. We conclude with a discussion of the ongoing plans from the project to develop learner-facing tools that enable awareness and self-reflection for online, self-directed learners. We also elucidate the need to establish further research programs on facets of self-tracking for learning that are necessarily going to emerge in the near future, especially regarding privacy and ethics.
2017

Ross-Hellauer Anthony, Deppe A., Schmidt B.

Survey on open peer review: Attitudes and experience amongst editors, authors and reviewers

Journal, PLOS One, 2017

Journal
Open peer review (OPR) is a cornerstone of the emergent Open Science agenda. Yet to date no large-scale survey of attitudes towards OPR amongst academic editors, authors, reviewers and publishers has been undertaken. This paper presents the findings of an online survey, conducted for the OpenAIRE2020 project during September and October 2016, that sought to bridge this information gap in order to aid the development of appropriate OPR approaches by providing evidence about attitudes towards and levels of experience with OPR. The results of this cross-disciplinary survey, which received 3,062 full responses, show the majority (60.3%) of respondents to be believe that OPR as a general concept should be mainstream scholarly practice (although attitudes to individual traits varied, and open identities peer review was not generally favoured). Respondents were also in favour of other areas of Open Science, like Open Access (88.2%) and Open Data (80.3%). Among respondents we observed high levels of experience with OPR, with three out of four (76.2%) reporting having taken part in an OPR process as author, reviewer or editor. There were also high levels of support for most of the traits of OPR, particularly open interaction, open reports and final-version commenting. Respondents were against opening reviewer identities to authors, however, with more than half believing it would make peer review worse. Overall satisfaction with the peer review system used by scholarly journals seems to strongly vary across disciplines. Taken together, these findings are very encouraging for OPR’s prospects for moving mainstream but indicate that due care must be taken to avoid a “one-size fits all” solution and to tailor such systems to differing (especially disciplinary) contexts. OPR is an evolving phenomenon and hence future studies are to be encouraged, especially to further explore differences between disciplines and monitor the evolution of attitudes.
2017

Pammer-Schindler Viktoria, Fessl Angela, Wesiak Gudrun, Feyertag Sandra, Rivera-Pelayo Verónica

In-app Reflection Guidance: Lessons Learned across Four Field Trials at the Workplace

IEEE Transactions on Learning Technologies, IEEE, 2017

Journal
This paper presents a concept for in-app reflection guidance and its evaluation in four work-related field trials. By synthesizing across four field trials, we can show that computer-based reflection guidance can function in the workplace, in the sense of being accepted as technology, being perceived as useful and leading to reflective learning. This is encouraging for all endeavours aiming to transfer existing knowledge on reflection supportive technology from educational settings to the workplace. However,reflective learning in our studies was mostly visible to limited depth in textual entries made in the applications themselves; and proactive reflection guidance technology like prompts were often found to be disruptive. We offer these two issues as highly relevant questions for future research.
2017

Seifert Christin, Bailer Werner, Orgel Thomas, Gantner Louis, Kern Roman, Ziak Hermann, Petit Albin, Schlötterer Jörg, Zwicklbauer Stefan, Granitzer Michael

Ubiquitous Access to Digital Cultural Heritage

Journal on Computing and Cultural Heritage (JOCCH) - Special Issue on Digital Infrastructure for Cultural Heritage, Part 1, Roberto Scopign, ACM, New York, NY, US, 2017

Journal
The digitization initiatives in the past decades have led to a tremendous increase in digitized objects in the cultural heritagedomain. Although digitally available, these objects are often not easily accessible for interested users because of the distributedallocation of the content in different repositories and the variety in data structure and standards. When users search for culturalcontent, they first need to identify the specific repository and then need to know how to search within this platform (e.g., usageof specific vocabulary). The goal of the EEXCESS project is to design and implement an infrastructure that enables ubiquitousaccess to digital cultural heritage content. Cultural content should be made available in the channels that users habituallyvisit and be tailored to their current context without the need to manually search multiple portals or content repositories. Torealize this goal, open-source software components and services have been developed that can either be used as an integratedinfrastructure or as modular components suitable to be integrated in other products and services. The EEXCESS modules andcomponents comprise (i) Web-based context detection, (ii) information retrieval-based, federated content aggregation, (iii) meta-data definition and mapping, and (iv) a component responsible for privacy preservation. Various applications have been realizedbased on these components that bring cultural content to the user in content consumption and content creation scenarios. Forexample, content consumption is realized by a browser extension generating automatic search queries from the current pagecontext and the focus paragraph and presenting related results aggregated from different data providers. A Google Docs add-onallows retrieval of relevant content aggregated from multiple data providers while collaboratively writing a document. Theserelevant resources then can be included in the current document either as citation, an image, or a link (with preview) withouthaving to leave disrupt the current writing task for an explicit search in various content providers’ portals.
2017

di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique

Supporting Exploratory Search with a Visual User-Driven Approach

ACM Transactions on Interactive Intelligent Systems, ACM, ACM, 2017

Journal
Whenever we gather or organize knowledge, the task of search-ing inevitably takes precedence. As exploration unfolds, it be-comes cumbersome to reorganize resources along new interests,as any new search brings new results. Despite huge advances inretrieval and recommender systems from the algorithmic point ofview, many real-world interfaces have remained largely unchanged:results appear in an infinite list ordered by relevance with respect tothe current query. We introduceuRank, a user-driven visual tool forexploration and discovery of textual document recommendations.It includes a view summarizing the content of the recommenda-tion set, combined with interactive methods for understanding, re-fining and reorganizing documents on-the-fly as information needsevolve. We provide a formal experiment showing thatuRankuserscan browse the document collection and efficiently gather items rel-evant to particular topics of interest with significantly lower cogni-tive load compared to traditional list-based representations.
2017

Strohmaier David, di Sciascio Maria Cecilia, Errecalde Marcelo, Veas Eduardo Enrique

WikiLyzer: Interactive Information Quality Assessment in Wikipedia

ACM Intelligent User Interfaces, 2017

Konferenz
Innovations in digital libraries and services enable users to access large amounts of data on demand. Yet, quality assessment of information encountered on the Internet remains an elusive open issue. For example, Wikipedia, one of the most visited platforms on the Web, hosts thousands of user-generated articles and undergoes 12 million edits/contributions per month. User-generated content is undoubtedly one of the keys to its success, but also a hindrance to good quality: contributions can be of poor quality because everyone, even anonymous users, can participate. Though Wikipedia has defined guidelines as to what makes the perfect article, authors find it difficult to assert whether their contributions comply with them and reviewers cannot cope with the ever growing amount of articles pending review. Great efforts have been invested in algorith-mic methods for automatic classification of Wikipedia articles (as featured or non-featured) and for quality flaw detection. However, little has been done to support quality assessment of user-generated content through interactive tools that allow for combining automatic methods and human intelligence. We developed WikiLyzer, a toolkit comprising three Web-based interactive graphic tools designed to assist (i) knowledge discovery experts in creating and testing metrics for quality measurement , (ii) users searching for good articles, and (iii) users that need to identify weaknesses to improve a particular article. A case study suggests that experts are able to create complex quality metrics with our tool and a report in a user study on its usefulness to identify high-quality content.
2017

Mohr Peter, Mandl David, Tatzgern Markus, Veas Eduardo Enrique, Schmalstieg Dieter, Kalkofen Denis

Retargeting Video Tutorials Showing Tools With Surface Contact to Augmented Reality

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, ACM, Denver, CO, USA, 2017

Konferenz
A video tutorial effectively conveys complex motions, butmay be hard to follow precisely because of its restriction toa predetermined viewpoint. Augmented reality (AR) tutori-als have been demonstrated to be more effective. We bringthe advantages of both together by interactively retargetingconventional, two-dimensional videos into three-dimensionalAR tutorials. Unlike previous work, we do not simply overlayvideo, but synthesize 3D-registered motion from the video.Since the information in the resulting AR tutorial is registeredto 3D objects, the user can freely change the viewpoint with-out degrading the experience. This approach applies to manystyles of video tutorials. In this work, we concentrate on aclass of tutorials which alter the surface of an object
2017

Müller-Putz G. R., Ofner P., Schwarz Andreas, Pereira J., Luzhnica Granit, di Sciascio Maria Cecilia, Veas Eduardo Enrique, Stein Sebastian, Williamson John, Murray-Smith Roderick, Escolano C., Montesano L., Hessing B., Schneiders M., Rupp R.

MoreGrasp: Restoration of upper limb function in individuals with high spinal cord injury by multimodal neuroprostheses for interaction in daily activities

7th Graz Brain-Computer Interface Conference 2017, Graz, 2017

Konferenz
The aim of the MoreGrasp project is to develop a non-invasive, multimodal user interface including a brain-computer interface(BCI)for intuitive control of a grasp neuroprosthesisto supportindividuals with high spinal cord injury(SCI)in everyday activities. We describe the current state of the project, including the EEG system, preliminary results of natural movements decoding in people with SCI, the new electrode concept for the grasp neuroprosthesis, the shared control architecture behind the system and the implementation ofa user-centered design.
2017

Kowald Dominik, Lex Elisabeth

Overcoming the Imbalance Between Tag Recommendation Approaches and Real-World Folksonomy Structures with Cognitive-Inspired Algorithm

European Symposium on Computational Social Sciences, ESCSS'2017, ACM, London, 2017

Konferenz
In this paper, we study the imbalance between current state-of-the-art tag recommendation algorithms and the folksonomy structures of real-world social tagging systems. While algorithms such as FolkRank are designed for dense folksonomy structures, most social tagging systems exhibit a sparse nature. To overcome this imbalance, we show that cognitive-inspired algorithms, which model the tag vocabulary of a user in a cognitive-plausible way, can be helpful. Our present approach does this via implementing the activation equation of the cognitive architecture ACT-R, which determines the usefulness of units in human memory (e.g., tags). In this sense, our long-term research goal is to design hybrid recommendation approaches, which combine the advantages of both worlds in order to adapt to the current setting (i.e., sparse vs. dense ones)
2017

Tschinkel Gerwald, Sabol Vedran

Evaluating the Memorability and Readability of Micro-Filter Visualisations

International Conference on Information Visualization Theory and Applications, Porto, Portugal, 2017

Konferenz
When using classical search engines, researchers are often confronted with a number of results far beyond what they can realistically manage to read; when this happens, recommender systems can help, by pointing users to the most valuable sources of information. In the course of a long-term research project, research into one area can extend over several days, weeks, or even months. Interruptions are unavoidable, and, when multiple team members have to discuss the status of a project, it’s important to be able to communicate the current research status easily and accurately. Multiple type-specific interactive views can help users identify the results most relevant to their focus of interest. Our recommendation dashboard uses micro-filter visualizations intended to improve the experience of working with multiple active filters, allowing researchers to maintain an overview of their progress. Within this paper, we carry out an evaluation of whether micro-visualizations help to increase the memorability and readability of active filters in comparison to textual filters. Five tasks, quantitative and qualitative questions, and the separate view on the different visualisation types enabled us to gain insights on how micro-visualisations behave and will be discussed throughout the paper.
2017

Lindstaedt Stefanie , Czech Paul, Fessl Angela

Theory of Knowledge Management

A Lifecycle Approach to Knowledge Excellence in the Biopharmaceutical Industry, Nuala Calnan, Martin J Lipa, Paige E. Kane, Jose C. Menezes, CRC Press, 2017

Buch
A Lifecycle Approach to Knowledge Excellence various industries and use cases. Through their cognitive computing-based approach, which combines the strength of man and the machine, they are setting standards within both the local and the international research community. With their expertise in the field of knowledge management they are describing the basic approaches in this chapter.
2017

Veas Eduardo Enrique

From Search to Discovery with Visual Exploration Tools

Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics, ACM, Limassol, Cyprus, 2017

Konferenz
In our goal to personalize the discovery of scientific information, we built systems using visual analytics principles for exploration of textual documents [1]. The concept was extended to explore information quality of user generated content [2]. Our interfaces build upon a cognitive model, where awareness is a key step of exploration [3]. In education-related circles, a frequent concern is that people increasingly need to know how to search, and that knowing how to search leads to finding information efficiently. The ever-growing information overabundance right at our fingertips needs a naturalskill to develop and refine search queries to get better search results, or does it?Exploratory search is an investigative behavior we adopt to build knowledge by iteratively selecting interesting features that lead to associations between representative items in the information space [4,5]. Formulating queries was proven more complicated for humans than recognizing information visually [6]. Visual analytics takes the form of an open ended dialog between the user and the underlying analytics algorithms operating on the data [7]. This talk describes studies on exploration and discovery with visual analytics interfaces that emphasize transparency and control featuresto trigger awareness. We will discuss the interface design and the studies of visual exploration behavior.
2017

di Sciascio Maria Cecilia, Mayr Lukas, Veas Eduardo Enrique

Exploring and Summarizing Document Colletions with Multiple Coordinated Views

Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics, ACM, Limassol, Cyprus, 2017

Konferenz
Knowledge work such as summarizing related research inpreparation for writing, typically requires the extraction ofuseful information from scientific literature. Nowadays theprimary source of information for researchers comes fromelectronic documents available on the Web, accessible throughgeneral and academic search engines such as Google Scholaror IEEE Xplore. Yet, the vast amount of resources makesretrieving only the most relevant results a difficult task. Asa consequence, researchers are often confronted with loadsof low-quality or irrelevant content. To address this issuewe introduce a novel system, which combines a rich, inter-active Web-based user interface and different visualizationapproaches. This system enables researchers to identify keyphrases matching current information needs and spot poten-tially relevant literature within hierarchical document collec-tions. The chosen context was the collection and summariza-tion of related work in preparation for scientific writing, thusthe system supports features such as bibliography and citationmanagement, document metadata extraction and a text editor.This paper introduces the design rationale and components ofthe PaperViz. Moreover, we report the insights gathered in aformative design study addressing usability
2017

Schrunner Stefan, Bluder Olivia, Zernig Anja, Kaestner Andre, Kern Roman

Markov Random Fields for Pattern Extraction in Analog Wafer Test Data

International Conference on Image Processing Theory, Tools and Applications (IPTA 2017), IEEE, Montreal, Canada, 2017

Konferenz
In semiconductor industry it is of paramount im- portance to check whether a manufactured device fulfills all quality specifications and is therefore suitable for being sold to the customer. The occurrence of specific spatial patterns within the so-called wafer test data, i.e. analog electric measurements, might point out on production issues. However the shape of these critical patterns is unknown. In this paper different kinds of process patterns are extracted from wafer test data by an image processing approach using Markov Random Field models for image restoration. The goal is to develop an automated procedure to identify visible patterns in wafer test data to improve pattern matching. This step is a necessary precondition for a subsequent root-cause analysis of these patterns. The developed pattern ex- traction algorithm yields a more accurate discrimination between distinct patterns, resulting in an improved pattern comparison than in the original dataset. In a next step pattern classification will be applied to improve the production process control.
2017

Rexha Andi, Kern Roman, Ziak Hermann, Dragoni Mauro

A semantic federated search engine for domain-specific document retrieval

SAC '17 Proceedings of the Symposium on Applied Computing, Sung Y. Shin, Dongwan Shin, Maria Lencastre, ACM, Marrakech, Morocco, 2017

Konferenz
Retrieval of domain-specific documents became attractive for theSemantic Web community due to the possibility of integrating classicInformation Retrieval (IR) techniques with semantic knowledge.Unfortunately, the gap between the construction of a full semanticsearch engine and the possibility of exploiting a repository ofontologies covering all possible domains is far from being filled.Recent solutions focused on the aggregation of different domain-specificrepositories managed by third-parties. In this paper, wepresent a semantic federated search engine developed in the contextof the EEXCESS EU project. Through the developed platform,users are able to perform federated queries over repositories in atransparent way, i.e. without knowing how their original queries aretransformed before being actually submitted. The platform implementsa facility for plugging new repositories and for creating, withthe support of general purpose knowledge bases, knowledge graphsdescribing the content of each connected repository. Such knowledgegraphs are then exploited for enriching queries performed byusers.
2017

Kern Roman, Falk Stefan, Rexha Andi

Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator

Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), Isabelle Augenstein, Mrinal Das, Sebastian Riedel, Lakshmi Vikraman, Andrew McCallum, ACL, Vancouver, Canada, 2017

Konferenz
This paper describes our participation inSemEval-2017 Task 10, named ScienceIE(Machine Reading for Scientist). We competedin Subtask 1 and 2 which consist respectivelyin identifying all the key phrasesin scientific publications and label them withone of the three categories: Task, Process,and Material. These scientific publicationsare selected from Computer Science, MaterialSciences, and Physics domains. We followeda supervised approach for both subtasksby using a sequential classifier (CRF - ConditionalRandom Fields). For generating oursolution we used a web-based application implementedin the EU-funded research project,named CODE. Our system achieved an F1score of 0.39 for the Subtask 1 and 0.28 forthe Subtask 2.
2017

Ross-Hellauer Anthony

What is open peer review? A systematic review [version 2; referees: 4 approved]

F1000Research, F1000, 2017

Journal
Background: “Open peer review” (OPR), despite being a major pillar of Open Science, has neither a standardized definition nor an agreed schema of its features and implementations. The literature reflects this, with numerous overlapping and contradictory definitions. While for some the term refers to peer review where the identities of both author and reviewer are disclosed to each other, for others it signifies systems where reviewer reports are published alongside articles. For others it signifies both of these conditions, and for yet others it describes systems where not only “invited experts” are able to comment. For still others, it includes a variety of combinations of these and other novel methods.Methods: Recognising the absence of a consensus view on what open peer review is, this article undertakes a systematic review of definitions of “open peer review” or “open review”, to create a corpus of 122 definitions. These definitions are systematically analysed to build a coherent typology of the various innovations in peer review signified by the term, and hence provide the precise technical definition currently lacking.Results: This quantifiable data yields rich information on the range and extent of differing definitions over time and by broad subject area. Quantifying definitions in this way allows us to accurately portray exactly how ambiguously the phrase “open peer review” has been used thus far, for the literature offers 22 distinct configurations of seven traits, effectively meaning that there are 22 different definitions of OPR in the literature reviewed.Conclusions: I propose a pragmatic definition of open peer review as an umbrella term for a number of overlapping ways that peer review models can be adapted in line with the aims of Open Science, including making reviewer and author identities open, publishing review reports and enabling greater participation in the peer review process.
2017

Dragoni Mauro, Federici Marco, Rexha Andi

Extracting Aspects From User-generated Content For Supporting Opinion Mining Systems

Journal of Intelligent Information Systems, Kerschberg; Z. Ras, Springer, 2017

Journal
One of the most important opinion mining research directions falls in the extraction ofpolarities referring to specific entities (aspects) contained in the analyzed texts. The detectionof such aspects may be very critical especially when documents come from unknowndomains. Indeed, while in some contexts it is possible to train domain-specificmodels for improving the effectiveness of aspects extraction algorithms, in others themost suitable solution is to apply unsupervised techniques by making such algorithmsdomain-independent. Moreover, an emerging need is to exploit the results of aspectbasedanalysis for triggering actions based on these data. This led to the necessityof providing solutions supporting both an effective analysis of user-generated contentand an efficient and intuitive way of visualizing collected data. In this work, we implementedan opinion monitoring service implementing (i) a set of unsupervised strategiesfor aspect-based opinion mining together with (ii) a monitoring tool supporting usersin visualizing analyzed data. The aspect extraction strategies are based on the use of semanticresources for performing the extraction of aspects from texts. The effectivenessof the platform has been tested on benchmarks provided by the SemEval campaign and have been compared with the results obtained by domain-adapted techniques.
2017

Luzhnica Granit, Veas Eduardo Enrique, Stein Sebastian, Pammer-Schindler Viktoria, Williamson John, Murray-Smith Roderick

Personalising Vibrotactile Displays through Perceptual Sensitivity Adjustment

Proceedings of the 2017 ACM International Symposium on Wearable Computing, ACM, Maui, Hawai, USA, 2017

Konferenz
Haptic displays are commonly limited to transmitting a dis- crete set of tactile motives. In this paper, we explore the transmission of real-valued information through vibrotactile displays. We simulate spatial continuity with three perceptual models commonly used to create phantom sensations: the lin- ear, logarithmic and power model. We show that these generic models lead to limited decoding precision, and propose a method for model personalization adjusting to idiosyncratic and spatial variations in perceptual sensitivity. We evaluate this approach using two haptic display layouts: circular, worn around the wrist and the upper arm, and straight, worn along the forearm. Results of a user study measuring continuous value decoding precision show that users were able to decode continuous values with relatively high accuracy (4.4% mean error), circular layouts performed particularly well, and per- sonalisation through sensitivity adjustment increased decoding precision.
2017

Luzhnica Granit, Veas Eduardo Enrique

Vibrotactile Patterns using Sensitivity Prioritisation

Proceedings of the 2017 ACM International Symposium on Wearable Computers, ACM, Maui, Hawai, USA, 2017

Konferenz
This paper investigates sensitivity based prioritisation in the construction of tactile patterns. Our evidence is obtained by three studies using a wearable haptic display with vibrotactile motors (tactors). Haptic displays intended to transmit symbols often suffer the tradeoff between throughput and accuracy. For a symbol encoded with more than one tactor simultaneous onsets (spatial encoding) yields the highest throughput at the expense of the accuracy. Sequential onset increases accuracy at the expense of throughput. In the desire to overcome these issues, we investigate aspects of prioritisation based on sensitivity applied to the encoding of haptics patterns. First, we investigate an encoding method using mixed intensities, where different body locations are simultaneously stimulated with different vibration intensities. We investigate whether prioritising the intensity based on sensitivity improves identification accuracy when compared to simple spatial encoding. Second, we investigate whether prioritising onset based on sensitivity affects the identification of overlapped spatiotemporal patterns. A user study shows that this method significantly increases the accuracy. Furthermore, in a third study, we identify three locations on the hand that lead to an accurate recall. Thereby, we design the layout of a haptic display equipped with eight tactors, capable of encoding 36 symbols with only one or two locations per symbol.
2016

Silva Nelson, Shao Lin, Schreck Tobias, Eggeling Eva, Fellner Dieter W.

Visual Exploration of Hierarchical Data Using Degree-of-Interest Controlled by Eye-Tracking

FMT 2016 : 9th Forum Media Technology 2016, Wolfgang Aigner , Grischa Schmiedl , Kerstin Blumenstein , Matthias Zeppelzauer , Michael Iber, St. Pölten, 2016

Konferenz
Effective visual exploration of large data sets is an important problem. A standard tech- nique for mapping large data sets is to use hierarchical data representations (trees, or dendrograms) that users may navigate. If the data sets get large, so do the hierar- chies, and effective methods for the naviga- tion are required. Traditionally, users navi- gate visual representations using desktop in- teraction modalities, including mouse interac- tion. Motivated by recent availability of low- cost eye-tracker systems, we investigate ap- plication possibilities to use eye-tracking for controlling the visual-interactive data explo- ration process. We implemented a proof-of- concept system for visual exploration of hier- archic data, exemplified by scatter plot dia- grams which are to be explored for grouping and similarity relationships. The exploration includes usage of degree-of-interest based dis- tortion controlled by user attention read from eye-movement behavior. We present the basic elements of our system, and give an illustra- tive use case discussion, outlining the applica- tion possibilities. We also identify interesting future developments based on the given data views and captured eye-tracking information. (13) Visual Exploration of Hierarchical Data Using Degree-of-Interest Controlled by Eye-Tracking. Available from: https://www.researchgate.net/publication/309479681_Visual_Exploration_of_Hierarchical_Data_Using_Degree-of-Interest_Controlled_by_Eye-Tracking [accessed Oct 3, 2017].
2016

Ayris Paul, Berthou Jean-Yves, Bruce Rachel, Lindstaedt Stefanie , Monreale Anna, Mons Barend, Murayama Yasuhiro, Södegard Caj, Tochtermann Klaus, Wilkinson Ross

Realising the European open science cloud

European Union, 2016

The European Open Science Cloud (EOSC) aims to accelerate and support the current transition to more effective Open Science and Open Innovation in the Digital Single Market. It should enable trusted access to services, systems and the re-use of shared scientific data across disciplinary, social and geographical borders. This report approaches the EOSC as a federated environment for scientific data sharing and re-use, based on existing and emerging elements in the Member States, with light-weight international guidance and governance, and a large degree of freedom regarding practical implementation.
2016

Lindstaedt Stefanie , Ley Tobias, Klamma Ralf, Wild Fridolin

Learning analytics for workplace and professional learning

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, ACM, 2016

Konferenz
Recognizing the need for addressing the rather fragmented character of research in this field, we have held a workshop on learning analytics for workplace and professional learning at the Learning Analytics and Knowledge (LAK) Conference. The workshop has taken a broad perspective, encompassing approaches from a number of previous traditions, such as adaptive learning, professional online communities, workplace learning and performance analytics. Being co-located with the LAK conference has provided an ideal venue for addressing common challenges and for benefiting from the strong research on learning analytics in other sectors that LAK has established. Learning Analytics for Workplace and Professional Learning is now on the research agenda of several ongoing EU projects, and therefore a number of follow-up activities are planned for strengthening integration in this emerging field.
2016

Ziak Hermann, Rexha Andi, Kern Roman

KNOW At The Social Book Search Lab 2016 Mining Track

CLEF 2016 Social Book Search Lab, Krisztian Balog, Linda Cappellato, Nicola Ferro,Craig Macdonald, Springer, Évora, Portugal, 2016

Konferenz
This paper describes our system for the mining task of theSocial Book Search Lab in 2016. The track consisted of two task, theclassification of book request postings and the task of linking book identifierswith references mentioned within the text. For the classificationtask we used text mining features like n-grams and vocabulary size, butalso included advanced features like average spelling errors found withinthe text. Here two datasets were provided by the organizers for this taskwhich were evaluated separately. The second task, the linking of booktitles to a work identifier, was addressed by an approach based on lookuptables. For the dataset of the first task our approach was ranked third,following two baseline approaches of the organizers with an accuracy of91 percent. For the second dataset we achieved second place with anaccuracy of 82 percent. Our approach secured the first place with anF-score of 33.50 for the second task.
2016

Yi-ling Lin, Parra Denis, Trattner Christoph, Brusilovsky Peter

Tag-Based Information Access in Image Collections: Insights from Log Analysis, Eye-Gaze Analysis, and User Feedback

IEEE Transactions on Systems, Man and Cybernetics: Systems (SMCA) , 2016

Journal
2016

Rexha Andi, Kern Roman, Dragoni Mauro , Kröll Mark

Exploiting Propositions for Opinion Mining

ESWC-16 Challenge on Semantic Sentiment Analysis, Springer Link, Springer-Verlag, Crete, Greece, 2016

Konferenz
With different social media and commercial platforms, users express their opinion about products in a textual form. Automatically extracting the polarity (i.e. whether the opinion is positive or negative) of a user can be useful for both actors: the online platform incorporating the feedback to improve their product as well as the client who might get recommendations according to his or her preferences. Different approaches for tackling the problem, have been suggested mainly using syntactic features. The “Challenge on Semantic Sentiment Analysis” aims to go beyond the word-level analysis by using semantic information. In this paper we propose a novel approach by employing the semantic information of grammatical unit called preposition. We try to drive the target of the review from the summary information, which serves as an input to identify the proposition in it. Our implementation relies on the hypothesis that the proposition expressing the target of the summary, usually containing the main polarity information.
2016

Berndt Rene, Silva Nelson, Edtmayr Thomas, Sunk Alexander, Krispel Ulrich, Caldera Christian, Eggeling Eva, Fellner Dieter W., Sihn Wilfried

VASCO - Mastering the Shoals of Value Stream Mapping

CONTENT 2016, The Eighth International conference on Creative Content Technologies, Hans-Werner Sehring, René Berndt, IARIA, Rome, Italy, 2016

Konferenz
Value stream mapping is a lean management method for analyzing and optimizing a series of events for production or services. Even today the first step in value stream analysis - the acquisition of the current state - is still created using pen & paper by physically visiting the production place. We capture a digital representation of how manufacturing processes look like in reality. The manufacturing processes can be represented and efficiently analyzed for future production planning by using a meta description together with a dependency graph. With our Value Stream Creator and explOrer (VASCO) we present a tool, which contributes to all parts of value stream analysis - from data acquisition, over planning, comparison with previous realities, up to simulation of future possible states.
2016

Atzmüller Martin, Alvin Chin, Trattner Christoph

Proceedings of the 7th International Workshop on Modeling Social Media (MSM’16) at the 25th ACM World Wide Web Conference WWW’16 conference

ACM WWW2016, ACM, Montreal, Canada, 2016

Buch
2016

Dennerlein Sebastian, Treasure-Jones Tamsin, Lex Elisabeth, Ley Tobias

The role of collaboration and shared understanding in interprofessional teamwork

AMEE - International Conference of Medical Education 2016, AMEE 2016, 2016

Journal
Background: Teamworking, within and acrosshealthcare organisations, is essential to deliverexcellent integrated care. Drawing upon an alternationof collaborative and cooperative phases, we exploredthis teamworking and respective technologicalsupport within UK Primary Care. Participants usedBits&Pieces (B&P), a sensemaking tool for tracedexperiences that allows sharing results and mutuallyelaborating them: i.e. cooperating and/orcollaborating.Summary of Work: We conducted a two month-longcase study involving six healthcare professionals. InB&P, they reviewed organizational processes, whichrequired the involvement of different professions ineither collaborative and/or cooperative manner. Weused system-usage data, interviews and qualitativeanalysis to understand the interplay of teamworkingpracticeand technology.Summary of Results: Within our analysis we mainlyidentified cooperation phases. In a f2f-meeting,professionals collaboratively identified subtasks andassigned individuals leading collaboration on them.However, these subtasks were undertaken asindividual sensemaking efforts and finally combined(i.e. cooperation). We found few examples ofreciprocal interpretation processes (i.e. collaboration):e.g. discussing problems during sensemaking ormonitoring other’s sensemaking-outcomes to makesuggestions.Discussion: These patterns suggest that collaborationin healthcare often helps to construct a minimalshared understanding (SU) of subtasks to engage incooperation, where individuals trust in other’scompetencies and autonomous completion. However,we also found that professionals with positivecollaboration history and deepened SU were willing toundertake subtasks collaboratively. It seems thatacquiring such deepened SU of concepts andmethods, leads to benefits that motivate professionalsto collaborate more.Conclusion: Healthcare is a challenging environmentrequiring interprofessional work across organisations.For effective teamwork, a deepened SU is crucial andboth cooperation and collaboration are required.However, we found a tendency of staff to rely mainlyon cooperation when working in teams and not fullyexplore benefits of collaboration.Take Home Messages: To maximise benefits ofinterprofessional working, tools for teamworkingshould support both cooperation and collaborationprocesses and scaffold the move between them
2016

Thalmann Stefan, Ilvonen Ilona, Manhart Markus , Sillaber Christian

Knowledge Protection for Digital Innovations: Integrating Six Perspectives

Proceedings of the 11th Pre-ICIS Workshop on Information Security and Privacy, AIS Electronic Library (AISeL), Dublin, Ireland, 2016

Konferenz
New ways of combining digital and physical innovations, as well as intensified inter-organizational collaborations, create new challenges to the protection of organizational knowledge. Existing research on knowledge protection is at an early stage and scattered among various research domains. This research-in-progress paper presents a plan for a structured literature review on knowledge protection, integrating the perspectives of the six base domains of knowledge, strategic, risk, intellectual property rights, innovation, and information technology security management. We define knowledge protection as a set of capabilities comprising and enforcing technical, organizational, and legal mechanisms to protect tacit and explicit knowledge necessary to generate or adopt innovations.
2016

Cik Michael, Hebenstreit Cornelia, Horn Christopher, Schulze Gunnar, Traub Matthias, Schweighofer Erich, Hötzendorf Walter, Fellendorf Martin

Using cell phone and social media data to enhance safety at mega events

Transportation Research Board (TRB) 96th Annual Meeting, Washington DC, 2016

Konferenz
Guaranteeing safety during mega events has always played a role for organizers, their security guards and the action force. This work was realized to enhance safety at mega events and demonstrations without the necessity of fixed installations. Therefore a low cost monitoring system supporting the organization and safety personnel was developed using cell phone data and social media data in combination with safety concepts to monitor safety during the event in real time. To provide the achieved results in real time to the event and safety personnel an application for a Tablet-PC was established. Two representative events were applied as case studies to test and evaluate the results and to check response and executability of the app on site. Because data privacy is increasingly important, legal experts were closely involved and provided legal support.
2016

Ziak Hermann, Kern Roman

KNOW At The Social Book Search Lab 2016 Suggestion Track

CLEF 2016 Social Book Search Lab, Krisztian Balog, Linda Cappellato, Nicola Ferro, Craig Macdonal, CEUR Workshop Proceeding, Évora, Portugal, 2016

Konferenz
Within this work represents the documentation of our ap-proach on the Social Book Search Lab 2016 where we took part in thesuggestion track. The main goal of the track was to create book recom-mendation for readers only based on their stated request within a forum.The forum entry contained further contextual information, like the user’scatalogue of already read books and the list of example books mentionedin the user’s request. The presented approach is mainly based on themetadata included in the book catalogue provided by the organizers ofthe task. With the help of a dedicated search index we extracted severalpotential book recommendations which were re-ranked by the use of anSVD based approach. Although our results did not meet our expectationwe consider it as first iteration towards a competitive solution.
2016

Luzhnica Granit, Öjeling Christoffer, Veas Eduardo Enrique, Pammer-Schindler Viktoria

Technical Concept and Technology Choices for Implementing a Tangible Version of the Sokoban Game

Mixed and Augmented Reality (ISMAR-Adjunct), 2016 IEEE International Symposium on, IEEE, Merida, Mexico, 2016

Konferenz
This paper presents and discusses the technical concept of a virtualreality version of the Sokoban game with a tangible interface. Theunderlying rationale is to provide spinal-cord injury patients whoare learning to use a neuroprosthesis to restore their capability ofgrasping with a game environment for training. We describe as rel-evant elements to be considered in such a gaming concept: input,output, virtual objects, physical objects, activity tracking and per-sonalised level recommender. Finally, we also describe our experi-ences with instantiating the overall concept with hand-held mobilephones, smart glasses and a head mounted cardboard setup
2016

Silva Nelson, Shao Lin, Schreck Tobias, Eggeling Eva, Fellner Dieter W.

Sense.me - Open Source Framework for the Exploration and Visualization of Eye Tracking Data

IEEEVis - Proc. IEEE Conference on Information Visualization, Baltimore, Maryland, USA, 2016

Konferenz
We present a new open-source prototype framework to exploreand visualize eye-tracking experiments data. Firstly, standard eyetrackersare used to record raw eye gaze data-points on user experiments.Secondly, the analyst can configure gaze analysis parameters,such as, the definition of areas of interest, multiple thresholdsor the labeling of special areas, and we upload the data to a searchserver. Thirdly, a faceted web interface for exploring and visualizingthe users’ eye gaze on a large number of areas of interest isavailable. Our framework integrates several common visualizationsand it also includes new combined representations like an eye analysisoverview and a clustered matrix that shows the attention timestrength between multiple areas of interest. The framework can bereadily used for the exploration of eye tracking experiments data.We make available the source code of our prototype framework foreye-tracking data analysis.
2016

Silva Nelson, Caldera Christian, Krispel Ulrich, Eggeling Eva, Sunk Alexander, Reisinger Gerhard, Sihn Wilfried, Fellner Dieter W.

VASCO - Digging the Dead Man's Chest of Value Streams

International Journal on Advances in Intelligent Systems, IARIA, 2016

Journal
Value stream mapping is a lean management method for analyzing and optimizing a series of events for production or services. Even today the first step in value stream analysis – the acquisition of the current state map – is still created using pen & paper by physically visiting the production line. We capture a digital representation of how manufacturing processes look like in reality. The manufacturing processes can be represented and efficiently analyzed for future production planning as a future state map by using a meta description together with a dependency graph. With VASCO we present a tool, which contributes to all parts of value stream analysis - from data acquisition, over analyzing, planning, comparison up to simulation of alternative future state maps.We call this a holistic approach for Value stream mapping including detailed analysis of lead time, productivity, space, distance, material disposal, energy and carbon dioxide equivalents – depending in a change of calculated direct product costs.
2016

Gursch Heimo, Kern Roman

Internet of Things meets Big Data: An Infrastructure to Collect, Connect, and Analyse Sensor Data

VDE Kongress 2016: Internet der Dinge (VDE Kongress 2016), DE Verlag GmbH, Berlin - Offenbach, Congress Center Rosengarten, Mannheim, Germany, 2016

Konferenz
Many different sensing, recording and transmitting platforms are offered on today’s market for Internet of Things (IoT) applications. But taking and transmitting measurements is just one part of a complete system. Also long time storage and processing of recorded sensor values are vital for IoT applications. Big Data technologies provide a rich variety of processing capabilities to analyse the recorded measurements. In this paper an architecture for recording, searching, and analysing sensor measurements is proposed. This architecture combines existing IoT and Big Data technologies to bridge the gap between recording, transmission, and persistency of raw sensor data on one side, and the analysis of data on Hadoop clusters on the other side. The proposed framework emphasises scalability and persistence of measurements as well as easy access to the data from a variety of different data analytics tools. To achieve this, a distributed architecture is designed offering three different views on the recorded sensor readouts. The proposed architecture is not targeted at one specific use-case, but is able to provide a platform for a large number of different services.
2016

Gursch Heimo, Körner Stefan, Krasser Hannes, Kern Roman

Parameter Forecasting for Vehicle Paint Quality Optimisation

Mensch und Computer 2016 – Workshopband, Benjamin Weyers, Anke Dittmar, Gesellschaft für Informatik e.V., Aachen, 2016

Konferenz
Painting a modern car involves applying many coats during a highly complex and automated process. The individual coats not only serve a decoration purpose but are also curial for protection from damage due to environmental influences, such as rust. For an optimal paint job, many parameters have to be optimised simultaneously. A forecasting model was created, which predicts the paint flaw probability for a given set of process parameters, to help the production managers modify the process parameters to achieve an optimal result. The mathematical model was based on historical process and quality observations. Production managers who are not familiar with the mathematical concept of the model can use it via an intuitive Web-based Graphical User Interface (Web-GUI). The Web-GUI offers production managers the ability to test process parameters and forecast the expected quality. The model can be used for optimising the process parameters in terms of quality and costs.
2016

Thalmann Stefan, Manhart Markus

Balancing Knowledge Protection and Sharing in Networks of SME

Proceedings of IKNOW 2016, 2016

Konferenz
Organizations join networks to acquire external knowledge. This is especially important for SMEs since they often lack resources and are dependent on external knowledge to achieve and sustain competitive advantage. However, finding the right balance between measures facilitating knowledge sharing and measures protecting knowledge is a challenge. Whilst sharing is the raison d’être of networks, neglecting knowledge protection can be also detrimental to network, e.g., lead to one-sided skimming of knowledge. We identified four practices SMEs currently apply to balance protection of competitive knowledge and knowledge sharing in the network: (a) share in subgroups with high trust, (b) share partial aspects of the knowledge base, (c) share with people with low proximities, and (d) share common knowledge and protect the crucial. We further found that the application of the practices depends on the maturity of the knowledge. Further, we discuss how the practices relate to organizational protection capabilities and how the network can provide IT to support the development of these capabilities.
2016

di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique

Rank As You Go: User-Driven Exploration of Search Results

ACM IUI 2016, ACM New York, NY, USA ©201, New York, 2016

Konferenz
Whenever users engage in gathering and organizing new information, searching and browsing activities emerge at the core of the exploration process. As the process unfolds and new knowledge is acquired, interest drifts occur inevitably and need to be accounted for. Despite the advances in retrieval and recommender algorithms, real-world interfaces have remained largely unchanged: results are delivered in a relevance-ranked list. However, it quickly becomes cumbersome to reorganize resources along new interests, as any new search brings new results. We introduce uRank and investigate interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. uRank includes views summarizing the contents of a recommendation set and interactive methods conveying the role of users' interests through a recommendation ranking. A formal evaluation showed that gathering items relevant to a particular topic of interest with uRank incurs in lower cognitive load compared to a traditional ranked list. A second study consisting in an ecological validation reports on usage patterns and usability of the various interaction techniques within a free, more natural setting.
2016

Hasani-Mavriqi Ilire, Geigl Florian, Pujari Suhbash Chandra, Lex Elisabeth, Helic Denis

The Influence of Social Status and Network Structure on Consensus Building in Collaboration Networks

Social Network Analysis and Mining, Reda Alhajj, Springer Vienna, 2016

Journal
In this paper, we study the process of opinion dynamics and consensus building in online collaboration systems, in which users interact with each other following their common interests and their social profiles. Specifically, we are interested in how users similarity and their social status in the community, as well as the interplay of those two factors influence the process of consensus dynamics. For our study, we simulate the diffusion of opinions in collaboration systems using the well-known Naming Game model, which we extend by incorporating an interaction mechanism based on user similarity and user social status. We conduct our experiments on collaborative datasets extracted from the Web. Our findings reveal that when users are guided by their similarity to other users, the process of consensus building in online collaboration systems is delayed. A suitable increase of influence of user social status on their actions can in turn facilitate this process. In summary, our results suggest that achieving an optimal consensus building process in collaboration systems requires an appropriate balance between those two factors.
2016

Kraker Peter, Dennerlein Sebastian, Dörler, D, Ferus, A, Gutounig Robert, Heigl, F., Kaier, C., Rieck Katharina, Šimukovic, E., Vignoli Michela

The Vienna Principles: A Vision for Scholarly Communication in the 21st Century.

15th Annual STS Conference Graz 2016 Track: The Politics of Open Science, OANA, Zenodo, 2016

Journal
Between April 2015 and June 2016, members of the Open Access Network Aus- tria (OANA) working group “Open Access and Scholarly Communication” met in Vienna to discuss a fundamental reform of the scholarly communication system.By scholarly communication we mean the processes of producing, reviewing, organising, disseminating and preserving scholarly knowledge1. Scholarly communication does not only concern researchers, but also society at large, especially students, educators, policy makers, public administrators, funders, librarians, journalists, practitioners, publishers, public and private organisations, and interested citizens.
2016

Mutlu Belgin, Sabol Vedran, Gursch Heimo, Kern Roman

From Data to Visualisations and Back: Selecting Visualisations Based on Data and System Design Considerations

arXiv, 2016

Konferenz
Graphical interfaces and interactive visualisations are typical mediators between human users and data analytics systems. HCI researchers and developers have to be able to understand both human needs and back-end data analytics. Participants of our tutorial will learn how visualisation and interface design can be combined with data analytics to provide better visualisations. In the first of three parts, the participants will learn about visualisations and how to appropriately select them. In the second part, restrictions and opportunities associated with different data analytics systems will be discussed. In the final part, the participants will have the opportunity to develop visualisations and interface designs under given scenarios of data and system settings.
2016

Gursch Heimo, Wuttei Andreas, Gangloff Theresa

Learning Systems for Manufacturing Management Support

Proceedings of the 1st International Workshop on Science, Application and Methods in Industry 4.0, Roman Kern, Gerald Reiner, Olivia Bluder, Graz, Austria, 2016

Konferenz
Highly optimised assembly lines are commonly used in various manufacturing domains, such as electronics, microchips, vehicles, electric appliances, etc. In the last decades manufacturers have installed software systems to control and optimise their shop foor processes. Machine Learning can enhance those systems by providing new insights derived from the previously captured data. This paper provides an overview of Machine Learning felds and an introduction to manufacturing management systems. These are followed by a discussion of research projects in the feld of applying Machine Learning solutions for condition monitoring, process control, scheduling, and predictive maintenance.
2016

Santos Tiago, Kern Roman

A Literature Survey of Early Time Series Classification and Deep Learning

SamI40 workshop at i-KNOW'16, 2016

Konferenz
This paper provides an overview of current literature on timeseries classification approaches, in particular of early timeseries classification.A very common and effective time series classification ap-proach is the 1-Nearest Neighbor classifier, with differentdistance measures such as the Euclidean or dynamic timewarping distances. This paper starts by reviewing thesebaseline methods.More recently, with the gain in popularity in the applica-tion of deep neural networks to the field of computer vision,research has focused on developing deep learning architec-tures for time series classification as well. The literature inthe field of deep learning for time series classification hasshown promising results.Early time series classification aims to classify a time se-ries with as few temporal observations as possible, whilekeeping the loss of classification accuracy at a minimum.Prominent early classification frameworks reviewed by thispaper include, but are not limited to, ECTS, RelClass andECDIRE. These works have shown that early time seriesclassification may be feasible and performant, but they alsoshow room for improvement
2016

Kern Roman, Ziak Hermann

Query Splitting For Context-Driven Federated Recommendations

Database and Expert Systems Applications (DEXA), 2016 27th International Workshop on, IEEEE, Porto, Portugal, 2016

Konferenz
Context-driven query extraction for content-basedrecommender systems faces the challenge of dealing with queriesof multiple topics. In contrast to manually entered queries, forautomatically generated queries this is a more frequent problem. For instances if the information need is inferred indirectly viathe user's current context. Especially for federated search systemswere connected knowledge sources might react vastly differentlyon such queries, an algorithmic way how to deal with suchqueries is of high importance. One such method is to split mixedqueries into their individual subtopics. To gain insight how amulti topic query can be split into its subtopics we conductedan evaluation where we compared a naive approach against amore complex approaches based on word embedding techniques:One created using Word2Vec and one created using GloVe. Toevaluate these two approaches we used the Webis-QSeC-10 queryset, consisting of about 5,000 multi term queries. Queries of thisset were concatenated and passed through the algorithms withthe goal to split those queries again. Hence the naive approach issplitting the queries into several groups, according to the amountof joined queries, assuming the topics are of equal query termcount. In the case of the Word2Vec and GloVe based approacheswe relied on the already pre-trained datasets. The Google Newsmodel and a model trained with a Wikipedia dump and theEnglish Gigaword newswire text archive. The out of this datasetsresulting query term vectors were grouped into subtopics usinga k-Means clustering. We show that a clustering approach basedon word vectors achieves better results in particular when thequery is not in topical order. Furthermore we could demonstratethe importance of the underlying dataset.
2016

Trattner Christoph, Elsweiler David, Howard Simon

Estimating the Healthiness of Internet recipes: Implications for Recommender Systems and Meal Planning

British Medical Journal (BMJ) , Frontiers in Public Health, 2016

Konferenz
One government response to increasing incidence of lifestyle related illnesses, such as obesity, has been to encourage people to cook for themselves. The healthiness of home cooking will, nevertheless, depend on what people cook and how they cook it. In this article one common source of cooking inspiration - Internet-sourced recipes - is investigated in depth. The energy and macronutrient content of 5237 main meal recipes from the food website Allrecipes.com are compared with those of 100 main meal recipes from five bestselling cookery books from popular celebrity chefs and 100 ready meals from the three leading UK supermarkets. The comparison is made using nutritional guidelines published by the World Health Organisation and the UK Food Standards Agency. The main conclusions drawn from our analyses are that Internet recipes sourced from Allrecipes.com are less healthy than TV-chef recipes and ready meals from leading UK supermarkets. Only 6 out of 5237 Internet recipes fully complied with the WHO recommendations. Internet recipes were more likely to meet the WHO guidelines for protein than both other classes of meal (10.88% v 7% (TV), p<0.01; 10.86% v 9% (ready), p<0.01). However, the Internet recipes were less likely to meet the criteria for fat (14.28% v 24% (TV) v 37% (ready); p<0.01), saturated fat (25.05% v 33% (TV) v 34% (ready); p<0.01) and fibre (compared to ready meals 16.50% v 56%; p<0.01). More Internet recipes met the criteria for sodium density than ready meals (19.63% v 4%; p<0.01), but fewer than the TV-chef meals (19.32% v 36%; p<0.01). For sugar, no differences between Internet recipes and TV-chef recipes were observed (81.1% v 81% (TV); p=0.86), although Internet recipes were less likely to meet the sugar criteria than ready meals (81.1% v 83 % (ready); p<0.01). Repeating the analyses for each year of available data shows that the results are very stable over time.
2016

Falk Stefan, Rexha Andi, Kern Roman

Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction

Conference: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), SemEval 2016, ACL Anthology, San Diego, USA, 2016

Konferenz
This paper describes our participation in SemEval-2016 Task 5 for Subtask 1, Slot 2.The challenge demands to find domain specific target expressions on sentence level thatrefer to reviewed entities. The detection of target words is achieved by using word vectorsand their grammatical dependency relationships to classify each word in a sentence into target or non-target. A heuristic based function then expands the classified target words tothe whole target phrase. Our system achievedan F1 score of 56.816% for this task.
2016

Horn Christopher, Gursch Heimo, Kern Roman, Cik Michael

QZTool – Automatically generated Origin-Destination Matrices from Cell Phone Trajectories

Advances in The Human Side of Service Engineering: Proceedings of the AHFE 2016 International Conference on Human Factors and Sustainable Infrastructure, July 27-31, 2016, Walt Disney World®, Florida, USA, Jerzy Charytonowicz (series Editor), Neville A. Stanton and Steven Landry and Giuseppe Di Bucchianico and Andrea Vallicelli, Springer International Publishing, Cham, Switzerland, 2016

Konferenz
Models describing human travel patterns are indispensable to plan and operate road, rail and public transportation networks. For most kind of analyses in the field of transportation planning, there is a need for origin-destination (OD) matrices, which specify the travel demands between the origin and destination zones in the network. The preparation of OD matrices is traditionally a time consuming and cumbersome task. The presented system, QZTool, reduces the necessary effort as it is capable of generating OD matrices automatically. These matrices are produced starting from floating phone data (FPD) as raw input. This raw input is processed by a Hadoop-based big data system. A graphical user interface allows for an easy usage and hides the complexity from the operator. For evaluation, we compare a FDP-based OD matrix to an OD matrix created by a traffic demand model. Results show that both matrices agree to a high degree, indicating that FPD-based OD matrices can be used to create new, or to validate or amend existing OD matrices.
2016

Urak Günter, Ziak Hermann, Kern Roman

Do Ambiguous Words Improve Probing For Federated Search?

International Conference on Theory and Practice of Digital Libraries, TPDL 2016, Springer-Verlag, 2016

Konferenz
The core approach to distributed knowledge bases is federated search. Two of the main challenges for federated search are the source representation and source selection. Different solutions to these problems were proposed in the literature. Within this work we present our novel approach for query-based sampling by relying on knowledge bases. We show the basic correctness of our approach and we came to the insight that the ambiguity of the probing terms has just a minor impact on the representation of the collection. Finally, we show that our method can be used to distinguish between niche and encyclopedic knowledge bases.
2016

Klampfl Stefan, Kern Roman

Reconstructing the Logical Structure of a Scientific Publication using Machine Learning

Semantic Web Challenges, Communications in Computer and Information Science, Springer Link, Springer-Verlag, 2016

Konferenz
Semantic enrichment of scientific publications has an increasing impact on scholarly communication. This document describes our contribution to Semantic Publishing Challenge 2016, which aims at investigating novel approaches for improving scholarly publishing through semantic technologies. We participated in Task 2 of this challenge, which requires the extraction of information from the content of a paper given as PDF. The extracted information allows answering queries about the paper’s internal organisation and the context in which it was written. We build upon our contribution to the previous edition of the challenge, where we categorised meta-data, such as authors and affiliations, and extracted funding information. Here we use unsupervised machine learning techniques in order to extend the analysis of the logical structure of the document as to identify section titles and captions of figures and tables. Furthermore, we employ clustering techniques to create the hierarchical table of contents of the article. Our system is modular in nature and allows a separate training of different stages on different training sets.
2016

Kowald Dominik, Lex Elisabeth

The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems

27th ACM Conference on Hypertext and Hypermedia, Hypertext'2016, ACM, Halifax, 2016

Konferenz
In this paper, we study factors that in uence tag reuse behavior in social tagging systems. Our work is guided by the activation equation of the cognitive model ACT-R, which states that the usefulness of information in human memory depends on the three factors usage frequency, recency and semantic context. It is our aim to shed light on the in uence of these factors on tag reuse. In our experiments, we utilize six datasets from the social tagging systems Flickr, CiteULike, BibSonomy, Delicious, LastFM and MovieLens, covering a range of various tagging settings. Our results con rm that frequency, recency and semantic context positively in uence the reuse probability of tags. However, the extent to which each factor individually in uences tag reuse strongly depends on the type of folksonomy present in a social tagging system. Our work can serve as guideline for researchers and developers of tag-based recommender systems when designing algorithms for social tagging environments.
2016

Santos Patricia, Dennerlein Sebastian, Theiler Dieter, Cook John, Treasure-Jones Tamsin, Holley Debbie, Kerr Micky , Atwell Graham, Kowald Dominik, Lex Elisabeth

Going beyond your Personal Learning Network, using Recommendations and Trust through a Multimedia Question-Answering Service for Decision-support: a Case Study in the Healthcare

Journal of Universal Computer Science, J.UCS, J. UCS Consortium, 2016

Journal
Social learning networks enable the sharing, transfer and enhancement of knowledge in the workplace that builds the ground to exchange informal learning practices. In this work, three healthcare networks are studied in order to understand how to enable the building, maintaining and activation of new contacts at work and the exchange of knowledge between them. By paying close attention to the needs of the practitioners, we aimed to understand how personal and social learning could be supported by technological services exploiting social networks and the respective traces reflected in the semantics. This paper presents a case study reporting on the results of two co-design sessions and elicits requirements showing the importance of scaffolding strategies in personal and shared learning networks. Besides, the significance of these strategies to aggregate trust among peers when sharing resources and decision-support when exchanging questions and answers. The outcome is a set of design criteria to be used for further technical development for a social tool. We conclude with the lessons learned and future work.
2016

Kraker Peter, Peters Isabella, Lex Elisabeth, Gumpenberger Christian , Gorraiz Juan

Research data explored: an extended analysis of citations and alt metrics

Journal of Scientometrics, Springer Link, Springer-Verlag, Cham, 2016

Journal
In this study, we explore the citedness of research data, its distribution overtime and its relation to the availability of a digital object identifier (DOI) in the ThomsonReuters database Data Citation Index (DCI). We investigate if cited research data ‘‘im-pacts’’ the (social) web, reflected by altmetrics scores, and if there is any relationshipbetween the number of citations and the sum of altmetrics scores from various social mediaplatforms. Three tools are used to collect altmetrics scores, namely PlumX, ImpactStory,and Altmetric.com, and the corresponding results are compared. We found that out of thethree altmetrics tools, PlumX has the best coverage. Our experiments revealed thatresearch data remain mostly uncited (about 85 %), although there has been an increase inciting data sets published since 2008. The percentage of the number of cited research datawith a DOI in DCI has decreased in the last years. Only nine repositories are responsible for research data with DOIs and two or more citations. The number of cited research datawith altmetrics ‘‘foot-prints’’ is even lower (4–9 %) but shows a higher coverage ofresearch data from the last decade. In our study, we also found no correlation between thenumber of citations and the total number of altmetrics scores. Yet, certain data types (i.e.survey, aggregate data, and sequence data) are more often cited and also receive higheraltmetrics scores. Additionally, we performed citation and altmetric analyses of allresearch data published between 2011 and 2013 in four different disciplines covered by theDCI. In general, these results correspond very well with the ones obtained for research datacited at least twice and also show low numbers in citations and in altmetrics. Finally, weobserved that there are disciplinary differences in the availability and extent of altmetricsscores.
2016

Dennerlein Sebastian, Ley Tobias, , Lex Elisabeth, Seitlinger Paul

Take up my Tags: Exploring Benefits of Collaborative Learning in a Social Tagging Field Study at the Workplace

European Conference on Technology Enhanced Learning (EC-TEL 2016), EC-TEL 2016, Springer-Verlag, Cham, 2016

Konferenz
In the digital realm, meaning making is reflected in the reciprocal manipulation of mediating artefacts. We understand uptake, i.e. interaction with and understanding of others’ artefact interpretations, as central mechanism and investigate its impact on individual and social learning at work. Results of our social tagging field study indicate that increased uptake of others’ tags is related to a higher shared understanding of collaborators as well as narrower and more elaborative exploration in individual information search. We attribute the social and individual impact to accommodative processes in the high uptake condition.
2016

Kusmierczyk Tomasz, Trattner Christoph, Nørvåg Kjetil

Understanding and Predicting Online Food Recipe Production Patterns

HT '16 Proceedings of the 27th ACM Conference on Hypertext and Social Media, ACM, Halifax, NS, Canada, 2016

Konferenz
Studying online food patterns has recently become an active fieldof research. While there are a growing body of studies that investi-gate how online food in consumed, little effort has been devoted yetto understand how online food recipes are being created. To con-tribute to this lack of knowledge in the area, we present in this paperthe results of a large-scale study that aims at understanding howhistorical, social and temporal factors impact on the online foodcreation process. Several experiments reveal the extent to whichvarious factors are useful in predicting future recipe production.
2016

Lacic Emanuel, Kowald Dominik, Lex Elisabeth

High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Reviews.

27th ACM Conference on Hypertext and Hypermedia, Hypertext'2016, ACM, Halifax, 2016

Konferenz
Air travel is one of the most frequently used means of transportation in our every-day life. Thus, it is not surprising that an increasing number of travelers share their experiences with airlines and airports in form of online reviews on the Web. In this work, we thrive to explain and uncover the features of airline reviews that contribute most to traveler satisfaction. To that end, we examine reviews crawled from the Skytrax air travel review portal. Skytrax provides four review categories to review airports, lounges, airlines and seats. Each review category consists of several five-star ratings as well as free-text review content. In this paper, we conducted a comprehensive feature study and we find that not only five-star rating information such as airport queuing time and lounge comfort highly correlate with traveler satisfaction but also textual features in the form of the inferred review text sentiment. Based on our findings, we created classifiers to predict traveler satisfaction using the best performing rating features. Our results reveal that given our methodology, traveler satisfaction can be predicted with high accuracy. Additionally, we find that training a model on the sentiment of the review text provides a competitive alternative when no five star rating information is available. We believe that our work is of interest for researchers in the area of modeling and predicting user satisfaction based on available review data on the Web.
2016

Luzhnica Granit, Simon Jörg Peter, Lex Elisabeth, Pammer-Schindler Viktoria

A Sliding Window Approach to Natural Hand Gesture Recognition using a Custom Data Glove

Proceedings of the IEEE 3DUI 2016 Symposium on 3D User Interfaces, IEEE, Greenville, SC, USA, 2016

Konferenz
This paper explores the recognition of hand gestures based on a dataglove equipped with motion, bending and pressure sensors. We se-lected 31 natural and interaction-oriented hand gestures that canbe adopted for general-purpose control of and communication withcomputing systems. The data glove is custom-built, and contains13 bend sensors, 7 motion sensors, 5 pressure sensors and a magne-tometer. We present the data collection experiment, as well as thedesign, selection and evaluation of a classification algorithm. As weuse a sliding window approach to data processing, our algorithm issuitable for stream data processing. Algorithm selection and featureengineering resulted in a combination of linear discriminant anal-ysis and logistic regression with which we achieve an accuracy ofover 98. 5% on a continuous data stream scenario. When removingthe computationally expensive FFT-based features, we still achievean accuracy of 98. 2%.
2016

Malarkodi C. S., Lex Elisabeth, Sobha Lalitha Devi

Named Entity Recognition for the Agricultural Domain

17th International Conference on Intelligent Text Processing and Computational Linguistics (CICLING 2016); Research in Computing Science, CICLING 2016, Springer Lecture Notes in Computer Science, Konya, Turkey, 2016

Konferenz
Agricultural data have a major role in the planning and success of rural development activi ties. Agriculturalists, planners, policy makers, gover n- ment officials, farmers and researchers require relevant information to trigger decision making processes. This paper presents our approach towards extracting named entities from real - world agricultura l data from different areas of agricu l- ture using Conditional Random Fields (CRFs). Specifically, we have created a Named Entity tagset consisting of 19 fine grained tags. To the best of our knowledge, there is no specific tag set and annotated corpus avail able for the agricultural domain. We have performed several experiments using different combination of features and obtained encouraging results. Most of the issues observed in an error analysis have been addressed by post - processing heuristic rules, which resulted in a significant improvement of our system’s accuracy
2016

Dennerlein Sebastian, Lex Elisabeth, Ruiz-Calleja Adolfo, Ley Elisabeth

Visualizing workplace learning data with the SSS Dashboard

Learning Analytics for Workplace and Professional Learning (LA for Work) workshop at LAK 2016, CEUR Workshop Proceedings, Edinburgh, 2016

Konferenz
This paper reports the design and development of a visual Dashboard, called the SSS Dashboard, which visualizes data from informal workplace learning processes from different viewpoints. The SSS Dashboard retrieves its data from the Social Semantic Server (SSS), an infrastructure that integrates data from several workplace learning applications into a semantically-enriched Artifact-Actor Network. A first evaluation with end users in a course for professional teachers gave promising results. Both a trainer and a learner could understand the learning process from different perspectives using the SSS Dashboard. The results obtained will pave the way for the development of future Learning Analytics applications that exploit the data collected by the SSS.
2016

Simon Jörg Peter, Schmidt Peter, Pammer-Schindler Viktoria

Analysis of Differential Synchronisation's Energy Consumption on Mobile Devices

EAI Collaborative Computing, CoRR (2016), EAI, 2016

Journal
Synchronisation algorithms are central to collaborative editing software. As collaboration is increasingly mediated by mobile devices, the energy efficiency for such algorithms is interest to a wide community of application developers. In this paper we explore the differential synchronisation (diffsync) algorithm with respect to energy consumption on mobile devices. Discussions within this paper are based on real usage data of PDF annotations via the Mendeley iOS app, which requires realtime synchronisation. We identify three areas for optimising diffsync: a.) Empty cycles in which no changes need to be processed b.) tail energy by adapting cycle intervals and c.) computational complexity. Following these considerations, we propose a push-based diffsync strategy in which synchronisation cycles are triggered when a device connects to the network or when a device is notified of changes.
2016

Czech Paul

Big Data Technologies in Healthcare

2016

Needs, opportunities and challenges
2016

Fessl Angela, Wesiak Gudrun, Pammer-Schindler Viktoria

A Reflective Quiz in a Professional Qualification Program for Stroke Nurses: A Field Trial

EC-TEL 2016 Proceedings, Springer Link, Springer-Verlag, Cham, 2016

Konferenz
Reflective learning is an important strategy to keep the vast body of theoretical knowledge fresh, stay up-to-date with new knowledge, and to relate theoretical knowledge to practical experience. In this work, we present a study situated in a qualification program for stroke nurses in Germany. In the seven-week study, $21$ stroke nurses used a quiz on medical knowledge as additional learning instrument. The quiz contained typical quiz questions (``content questions'') as well as reflective questions that aimed at stimulating nurses to reflect on the practical relevance of the learned knowledge.We particularly looked at how reflective questions can support the transfer of theoretical knowledge to practice.The results show that by playful learning and presenting reflective questions at the right time, participants were motivated to reflect, deepened their knowledge and related theoretical knowledge to practical experience. Subsequently, they were able to better understand patient treatments and increased their self-confidence.
2016

Trattner Christoph, Kowald Dominik, Seitlinger Paul, Ley Tobias

Modeling Activation Processes in Human Memory to Predict the Reuse of Tags

The Journal of Web Science, James Finlay, NOW publishing, 2016

Journal
Several successful tag recommendation mechanisms have been developed, including algorithms built upon Collaborative Filtering, Tensor Factorization, graph-based and simple "most popular tags" approaches. From an economic perspective, the latter approach has been convincing since calculating frequencies is computationally efficient and effective with respect to different recommender evaluation metrics. In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory in order to extend these conventional "most popular tags" approaches. Based on a theory of human memory, the approach estimates a tag's reuse probability as a function of usage frequency and recency in the user's past (base-level activation) as well as of the current semantic context (associative component).Using four real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike, Delicious and Flickr, we show how refining frequency-based estimates by considering recency and semantic context outperforms conventional "most popular tags" approaches and another existing and very effective but less theory-driven, time-dependent recommendation mechanism. By combining our approach with a simple resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as Collaborative Filtering, FolkRank and Pairwise Interaction Tensor Factorization with respect to recommender accuracy and runtime. We conclude that our approach provides an accurate and computationally efficient model of a user's temporal tagging behavior. Moreover, we demonstrate how effective principles of recommender systems can be designed and implemented if human memory processes are taken into account.
2016

Luzhnica Granit, Veas Eduardo Enrique, Pammer-Schindler Viktoria

Skin Reading: Encoding Text in a 6-Channel Haptic Display

Proceedings of the 2016 ACM International Symposium on Wearable Computing, ACM, Heidelberg, 2016

Konferenz
This paper investigates the communication of natural lan-guage messages using a wearable haptic display. Our re-search spans both the design of the haptic display, as wellas the methods for communication that use it. First, threewearable configurations are proposed basing on haptic per-ception fundamentals. To encode symbols, we devise an over-lapping spatiotemporal stimulation (OST) method, that dis-tributes stimuli spatially and temporally with a minima gap.An empirical study shows that, compared with spatial stimu-lation, OST is preferred in terms of recall. Second, we pro-pose an encoding for the entire English alphabet and a train-ing method for letters, words and phrases. A second study in-vestigates communication accuracy. It puts four participantsthrough five sessions, for an overall training time of approx-imately 5 hours per participant. Results reveal that after onehour of training, participants were able to discern 16 letters,and identify two- and three-letter words. They could discernthe full English alphabet (26letters,92%accuracy) after ap-proximately three hours of training, and after five hours par-ticipants were able to interpret words transmitted at an aver-age duration of0.6s per word
2016

Luzhnica Granit, Pammer-Schindler Viktoria, Fessl Angela, Mutlu Belgin, Veas Eduardo Enrique

Designing Generic Visualisations for Activity Log Data

Workshop on Awareness and Reflection in Technology Enhanced Learning (ARTEL16), CEUR-WS, Lyon, 2016

Konferenz
Especially in lifelong or professional learning, the picture of a continuous learning analytics process emerges. In this proces s, het- erogeneous and changing data source applications provide data relevant to learning, at the same time as questions of learners to data cha nge. This reality challenges designers of analytics tools, as it req uires ana- lytics tools to deal with data and analytics tasks that are unk nown at application design time. In this paper, we describe a generic vi sualiza- tion tool that addresses these challenges by enabling the vis ualization of any activity log data. Furthermore, we evaluate how well parti cipants can answer questions about underlying data given such generic versus custom visualizations. Study participants performed better in 5 out of 10 tasks with the generic visualization tool, worse in 1 out of 1 0 tasks, and without significant difference when compared to the visuali zations within the data-source applications in the remaining 4 of 10 ta sks. The experiment clearly showcases that overall, generic, standalon e visualiza- tion tools have the potential to support analytical tasks suffi ciently well
2016

Trattner Christoph, Schäfer Hanna, Said Alan, Ludwig Bernd, Elsweiler David

Proceedings of the International Workshop on Engendering Health

10th ACM Conference on Recommender Systems, ACM, Boston, 2016

Buch
Busy lifestyles, abundant options, lack of knowledge ... there are many reasons why people make poor decisions relating to their health. Yet these poor decisions are leading to epidemics, which represent some of the greatest challenges we face as a society today. Noncommunicable Diseases (NCDs), which include cardiovascular diseases, cancer, chronic respiratory diseases and diabetes, account for ∼60% of total deaths worldwide. These diseases share the same four behavioural risk factors: tobacco use, unhealthy diet, physical inactivity and harmful consumption of alcohol and can be prevented and sometimes even reversed with simple lifestyle changes. Eating more healthily, exercising more appropriately, sleeping and relaxing more, as well as simply being more aware of one’s state of health are all things that would lead to improved health. Yet knowing exactly what to change and how, implementing changes and maintaining changes over long time periods are all things people find challenging. These are also problems, for which we believe recommender systems can provide assistance by offering specific, tailored suggestions for behavioural change. In recent years recommender systems for health has become a popular topic within the RecSys community and a selection of empirical contributions and demo systems have been published. Efforts to date, however have been sporadic and lack coordination. We lack shared infrastructure such as datasets, appropriate cross-disciplinary knowledge, even agreed upon goals. It is our aim to use this workshop as a vehicle to:
2016

Atzmüller Martin, Chin Alvin, Trattner Christoph

Proceedings of the 7th International Workshop on Modeling Social Media

25th International World Wide Web Conference, MSM 2017, Montreal, 2016

Buch
For the 7h International Workshop on Modeling Social Media, we aim to attract researchers from all over the world working in the field of behavioral analytics using web and social media data. Behavioral analytics is an important topic, e.g., concerning web applications as well as extensions in mobile and ubiquitous applications, for understanding user behavior. We would also like to invite researchers in the data and web mining community to lend their expertise to help to increase our understanding of the web and social media.
2016

Eberhard Lukas, Trattner Christoph

Recommending Sellers to Buyers in Virtual Marketplaces Leveraging Social Information

WWW '16 Companion Proceedings of the 25th International Conference Companion on World Wide Web, WWW '16, Canton of Geneva, 2016

Konferenz
Social information such as stated interests or geographic check-insin social networks has shown to be useful in many recommendertasks recently. Although many successful examples exist, not muchattention has been put on exploring the extent to which social im-pact is useful for the task of recommending sellers to buyers in vir-tual marketplaces. To contribute to this sparse field of research wecollected data of a marketplace and a social network in the virtualworld of Second Life and introduced several social features andsimilarity metrics that we used as input for a user-basedk-nearestneighbor collaborative filtering method. As our results reveal, mostof the types of social information and features which we used areuseful to tackle the problem we defined. Social information suchas joined groups or stated interests are more useful, while otherssuch as places users have been checking in, do not help much forrecommending sellers to buyers. Furthermore, we find that some ofthe features significantly vary in their predictive power over time,while others show more stable behaviors. This research is rele-vant for researchers interested in recommender systems and onlinemarketplace research as well as for engineers interested in featureengineering.
2016

Kraker Peter, Kittel Christopher, Enkhbayar Asuraa

Open Knowledge Maps: Creating a Visual Interface to the World’s Scientific Knowledge Based on Natural Language Processing

027.7 Journal for Library Culture, 2016

Journal
The goal of Open Knowledge Maps is to create a visual interface to the world’s scientific knowledge. The base for this visual interface consists of so-called knowledge maps, which enable the exploration of existing knowledge and the discovery of new knowledge. Our open source knowledge mapping software applies a mixture of summarization techniques and similarity measures on article metadata, which are iteratively chained together. After processing, the representation is saved in a database for use in a web visualization. In the future, we want to create a space for collective knowledge mapping that brings together individuals and communities involved in exploration and discovery. We want to enable people to guide each other in their discovery by collaboratively annotating and modifying the automatically created maps.
2016

Kopeinik Simone, Kowald Dominik, Hasani-Mavriqi Ilire, Lex Elisabeth

Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning

Journal of WebScience, James Finlay, Now publishing, 2016

Journal
Classic resource recommenders like Collaborative Filteringtreat users as just another entity, thereby neglecting non-linear user-resource dynamics that shape attention and in-terpretation. SUSTAIN, as an unsupervised human cate-gory learning model, captures these dynamics. It aims tomimic a learner’s categorization behavior. In this paper, weuse three social bookmarking datasets gathered from Bib-Sonomy, CiteULike and Delicious to investigate SUSTAINas a user modeling approach to re-rank and enrich Collab-orative Filtering following a hybrid recommender strategy.Evaluations against baseline algorithms in terms of recom-mender accuracy and computational complexity reveal en-couraging results. Our approach substantially improves Col-laborative Filtering and, depending on the dataset, success-fully competes with a computationally much more expen-sive Matrix Factorization variant. In a further step, we ex-plore SUSTAIN’s dynamics in our specific learning task andshow that both memorization of a user’s history and clus-tering, contribute to the algorithm’s performance. Finally,we observe that the users’ attentional foci determined bySUSTAIN correlate with the users’ level of curiosity, iden-tified by the SPEAR algorithm. Overall, the results ofour study show that SUSTAIN can be used to efficientlymodel attention-interpretation dynamics of users and canhelp improve Collaborative Filtering for resource recommen-dations.
2016

Stanisavljevic Darko, Hasani-Mavriqi Ilire, Lex Elisabeth, Strohmaier M., Helic Denis

Semantic Stability in Wikipedia

Complex Networks and their Applications, Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A., Springer International Publishing AG, Cham, Switzerland, 2016

Konferenz
In this paper we assess the semantic stability of Wikipedia by investigat-ing the dynamics of Wikipedia articles’ revisions over time. In a semantically stablesystem, articles are infrequently edited, whereas in unstable systems, article contentchanges more frequently. In other words, in a stable system, the Wikipedia com-munity has reached consensus on the majority of articles. In our work, we measuresemantic stability using the Rank Biased Overlap method. To that end, we prepro-cess Wikipedia dumps to obtain a sequence of plain-text article revisions, whereaseach revision is represented as a TF-IDF vector. To measure the similarity betweenconsequent article revisions, we calculate Rank Biased Overlap on subsequent termvectors. We evaluate our approach on 10 Wikipedia language editions includingthe five largest language editions as well as five randomly selected small languageeditions. Our experimental results reveal that even in policy driven collaborationnetworks such as Wikipedia, semantic stability can be achieved. However, there aredifferences on the velocity of the semantic stability process between small and largeWikipedia editions. Small editions exhibit faster and higher semantic stability than large ones. In particular, in large Wikipedia editions, a higher number of successiverevisions is needed in order to reach a certain semantic stability level, whereas, insmall Wikipedia editions, the number of needed successive revisions is much lowerfor the same level of semantic stability.
2016

Kowald Dominik, Lex Elisabeth, Kopeinik Simone

Which Algorithms Suit Which Learning Environments? A Comparative Study of Recommender Systems in TEL

European Conference on Technology Enhanced Learning, EC-TEL'2016, Springer, Toledo, Spain, 2016

Konferenz
In recent years, a number of recommendation algorithmshave been proposed to help learners find suitable learning resources online.Next to user-centered evaluations, offline-datasets have been usedto investigate new recommendation algorithms or variations of collaborativefiltering approaches. However, a more extensive study comparinga variety of recommendation strategies on multiple TEL datasets ismissing. In this work, we contribute with a data-driven study of recommendationstrategies in TEL to shed light on their suitability forTEL datasets. To that end, we evaluate six state-of-the-art recommendationalgorithms for tag and resource recommendations on six empiricaldatasets: a dataset from European Schoolnets TravelWell, a dataset fromthe MACE portal, which features access to meta-data-enriched learningresources from the field of architecture, two datasets from the socialbookmarking systems BibSonomy and CiteULike, a MOOC dataset fromthe KDD challenge 2015, and Aposdle, a small-scale workplace learningdataset. We highlight strengths and shortcomings of the discussed recommendationalgorithms and their applicability to the TEL datasets.Our results demonstrate that the performance of the algorithms stronglydepends on the properties and characteristics of the particular dataset.However, we also find a strong correlation between the average numberof users per resource and the algorithm performance. A tag recommenderevaluation experiment reveals that a hybrid combination of a cognitiveinspiredand a popularity-based approach consistently performs best onall TEL datasets we utilized in our study.
2016

Goldgruber Eva, Gutounig Robert, Schweiger Stefan, Dennerlein Sebastian

Potential von "Slack" im E-Learning

E-Learning Tag 2016, 2016

Konferenz
2016

Gutounig Robert, Goldgruber Eva, Dennerlein Sebastian, Schweiger Stefan

Mehr als ein Kommunikationstool. Wissensmanagement-Potenziale von Social Software am Beispiel von Slack

Kremser Wissensmanagement-Tagen 2016, Edition Donau-Universität Krems , Krems, 2016

Konferenz
2016

Tschinkel Gerwald, Hasitschka Peter, Sabol Vedran, Hafner R

Using Micro-Visualisations to Support Faceted Filtering of Recommender Results

Information Visualisation (IV), 2016 20th International Conference, IEEE, Lisbon, Portugal, 2016

Konferenz
Faceted search is a well known and broadly imple- mented paradigm for filtering information with various types of structured information. In this paper we introduce a multiple-view faceted interface, consisting of one main visualisation for exploring the data and multiple minia- turised visualisations showing the filters. The Recommen- dation Dashboard tool provides several interactive visual- isations for analysing recommender results along various faceted dimensions specific to cultural heritage and scien- tific content. As our aim is to reduce the user load and opti- mise the use of screen area, we permit only one main visu- alisation to be visible at a time, and introduce the concept of micro-visualisations – small, simplified views conveying only the necessary information – to provide natural, easy to understand representation of the the active filter set.
2016

Luzhnica Granit, Veas Eduardo Enrique, Pammer-Schindler Viktoria

Technical Concept and Technology Choices for Implementing a Tangible Version of the Sokoban Gamehoices for Implementing a Tangible Version of the Sokoban Game

2016 IEEE International Symposium on Mixed and Augmented Reality Adjunct Proceedings ISMAR 2016, 2016

Konferenz
This paper presents and discusses the technical concept of a virtualreality version of the Sokoban game with a tangible interface. Theunderlying rationale is to provide spinal-cord injury patients whoare learning to use a neuroprosthesis to restore their capability ofgrasping with a game environment for training. We describe as rel-evant elements to be considered in such a gaming concept: input,output, virtual objects, physical objects, activity tracking and per-sonalised level recommender. Finally, we also describe our experi-ences with instantiating the overall concept with hand-held mobilephones, smart glasses and a head mounted cardboard setup.Index Terms: H.5.2 [HCI]: User Interfaces—Input devicesand strategies; H.5.1 [HCI]: Multimedia Information Systems—Artificial, augmented, and virtual realities
2016

Dennerlein Sebastian, Gutounig Robert, Goldgruber Eva , Schweiger Stefan

Web 2.0 Messaging Tools for Knowledge Management? Exploring the Potentials of Slack

Proceedings of the 17th European Conference on Knowledge Management, Academic Conferences International Limited, Ulster University, Northern Ireland, 2016

Konferenz
There are many web-based tools like social networks, collaborative writing, or messaging tools that connectorganizations in accordance with web 2.0 principles. Slack is such a web 2.0 instant messaging tool. As per developer, itintegrates the entire communication, file-sharing, real-time messaging, digital archiving and search at one place. Usage inline with these functionalities would reflect expected appropriation, while other usage would account for unexpectedappropriation. We explored which factors of web 2.0 tools determine actual usage and how they affect knowledgemanagement (KM). Therefore, we investigated the relation between the three influencing factors, proposed tool utility fromdeveloper side, intended usage of key implementers, and context of application, to the actual usage in terms of knowledgeactivities (generate, acquire, organize, transfer and save knowledge). We conducted episodic interviews with keyimplementers in five different organizational contexts to understand how messaging tools affect KM by analyzing theappropriation of features. Slack was implemented with the intention to enable exchange between project teams, connectingdistributed project members, initiate a community of learners and establish a communication platform. Independent of thecontext, all key implementers agreed on knowledge transfer, organization and saving in accordance with Slack’s proposedutility. Moreover, results revealed that a usage intention of internal management does not lead to acquisition of externalknowledge, and usage intention of networking not to generation of new knowledge. These results suggest that it is not thecontext of application, but the intended usage that mainly affects the tool's efficacy with respect to KM: I.e. intention seemsto affect tool selection, first, explaining commonalities with respect to knowledge activities (expected appropriation) and,subsequently, intention also affects unexpected appropriation beyond the developers’ tool utility. A messaging tool is, hence,not only a messaging tool, but it is ‘what you make of it!’
2016

Steinbauer Florian, Kröll Mark

Sentiment Analysis for German Facebook Pages

21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Springer-Verlag, Salford, UK, 2016

Konferenz
Social media monitoring has become an important means forbusiness analytics and trend detection, for instance, analyzing the senti-ment towards a certain product or decision. While a lot of work has beendedicated to analyze sentiment for English texts, much less effort hasbeen put into providing accurate sentiment classification for the Germanlanguage. In this paper, we analyze three established classifiers for theGerman language with respect to Facebook posts. We then present ourown hierarchical approach to classify sentiment and evaluate it using adata set of∼640 Facebook posts from corporate as well as governmentalFacebook pages. We compare our approach to three sentiment classifiersfor German, i.e. AlchemyAPI, Semantria and SentiStrength. With anaccuracy of 70 %, our approach performs better than the other classi-fiers. In an application scenario, we demonstrate our classifier’s abilityto monitor changes in sentiment with respect to the refugee crisis.
2016

Lacic Emanuel

Real-Time Recommendations in a Multi-Domain Environment

ACM Hypertext Doctoral Consortium, ACM, 2016

Konferenz
Recommender systems are acknowledged as an essential instru- ment to support users in finding relevant information. However, adapting to different domain specific data models is a challenge, which many recommender frameworks neglect. Moreover, the ad- vent of the big data era has posed the need for high scalability and real-time processing of frequent data updates, and thus, has brought new challenges for the recommender systems’ research community. In this work, we show how different item, social and location data features can be utilized and supported to provide real-time recom- mendations. We further show how to process data updates online and capture user’s real-time interest without recalculating recom- mendations. The presented recommendation framework provides a scalable and customizable architecture suited for providing real- time recommendations to multiple domains. We further investigate the impact of an increasing request load and show how the runtime can be decreased by scaling the framework.
2016

Pimas Oliver, Klampfl Stefan, Kohl Thomas, Kern Roman, Kröll Mark

Generating Tailored Classification Schemas for German Patents

21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Springer-Verlag, Salford, UK, 2016

Konferenz
Patents and patent applications are important parts of acompany’s intellectual property. Thus, companies put a lot of effort indesigning and maintaining an internal structure for organizing their ownpatent portfolios, but also in keeping track of competitor’s patent port-folios. Yet, official classification schemas offered by patent offices (i) areoften too coarse and (ii) are not mappable, for instance, to a company’sfunctions, applications, or divisions. In this work, we present a first steptowards generating tailored classification. To automate the generationprocess, we apply key term extraction and topic modelling algorithmsto 2.131 publications of German patent applications. To infer categories,we apply topic modelling to the patent collection. We evaluate the map-ping of the topics found via the Latent Dirichlet Allocation method tothe classes present in the patent collection as assigned by the domainexpert.
2016

Dragoni Mauro, Rexha Andi, Kröll Mark, Kern Roman

Polarity Classification for Target Phrases in Tweets: A Word2Vec approach

The Semantic Web, ESWC 2016 Satellite Events, ESWC 2016, Springer-Verlag, Crete, Greece, 2016

Konferenz
Twitter is one of the most popular micro-blogging serviceson the web. The service allows sharing, interaction and collaboration viashort, informal and often unstructured messages called tweets. Polarityclassification of tweets refers to the task of assigning a positive or a nega-tive sentiment to an entire tweet. Quite similar is predicting the polarityof a specific target phrase, for instance@Microsoftor#Linux,whichiscontained in the tweet.In this paper we present a Word2Vec approach to automatically pre-dict the polarity of a target phrase in a tweet. In our classification setting,we thus do not have any polarity information but use only semantic infor-mation provided by a Word2Vec model trained on Twitter messages. Toevaluate our feature representation approach, we apply well-establishedclassification algorithms such as the Support Vector Machine and NaiveBayes. For the evaluation we used theSemeval 2016 Task #4dataset.Our approach achieves F1-measures of up to∼90 % for the positive classand∼54 % for the negative class without using polarity informationabout single words.
2016

Barreiros Carla, Veas Eduardo Enrique, Pammer-Schindler Viktoria

Pre-attentive Features in Natural Augmented Reality Visualizations

2016 IEEE International Symposium on Mixed and Augmented Reality Adjunct Proceedings ISMAR, 2016 IEEE International Symposium on Mixed and Augmented Reality Adjunct Proceedings, Merida, Yucatan, Mexico, 2016

Konferenz
The movement towards cyberphysical systems and Industry 4.0promises to imbue each and every stage of production with a myr-iad of sensors. The open question is how people are to comprehendand interact with data originating from industrial machinery. Wepropose a metaphor that compares machines with natural beingsthat appeal to people by representing machine states with patternsoccurring in nature. Our approach uses augmented reality (AR)to represent machine states as trees of different shapes and col-ors (BioAR). We performed a study on pre-attentive processing ofvisual features in AR to determine if our BioAR metaphor con-veys fast changes unambiguously and accurately. Our results indi-cate that the visual features in our BioAR metaphor are processedpre-attentively. In contrast to previous research, for the BioARmetaphor, variations in form induced less errors than variations inhue in the target detection task.
2016

Rexha Andi, Klampfl Stefan, Kröll Mark, Kern Roman

Towards a more fine grained analysis of scientific authorship: Predicting the number of authors using stylometric features

BIR 2016 Workshop on Bibliometric-enhanced Information Retrieval, Atanassova, I.; Bertin, M.; Mayr, P., Springer, Padova, Italy, 2016

Konferenz
To bring bibliometrics and information retrieval closer together, we propose to add the concept of author attribution into the pre-processing of scientific publications. Presently, common bibliographic metrics often attribute the entire article to all the authors affecting author-specific retrieval processes. We envision a more finegrained analysis of scientific authorship by attributing particular segments to authors. To realize this vision, we propose a new feature representation of scientific publications that captures the distribution of tylometric features. In a classification setting, we then seek to predict the number of authors of a scientific article. We evaluate our approach on a data set of ~ 6100 PubMed articles and achieve best results by applying random forests, i.e., 0.76 precision and 0.76 recall averaged over all classes.
2016

Fessl Angela, Pammer-Schindler Viktoria, Blunk Oliver, Prilla Michael

The known universe of reflection guidance: a literature review

International Journal of Technology Enhanced Learning, Inderscience Enterprises Ltd., 2016

Journal
Reflective learning has been established as a process that deepenslearning in both educational and work-related settings. We present a literaturereview on various approaches and tools (e.g., prompts, journals, visuals)providing guidance for facilitating reflective learning. Research consideredin this review coincides common understanding of reflective learning, hasapplied and evaluated a tool supporting reflection and presents correspondingresults. Literature was analysed with respect to timing of reflection, reflectionparticipants, type of reflection guidance, and results achieved regardingreflection. From this analysis, we were able to derive insights, guidelinesand recommendations for the design of reflection guidance functionality incomputing systems: (i) ensure that learners understand the purpose of reflectivelearning, (ii) combine reflective learning tools with reflective questions either inform of prompts or with peer-to-peer or group discussions, (iii) for work-relatedsettings consider the time with regard to when and how to motivate to reflect.
2016

Traub Matthias, Lacic Emanuel, Kowald Dominik, Kahr Martin, Lex Elisabeth

Need Help? Recommending Social Care Institutions

Workshop on Recommender Systems and Big Data Analytics co-located with i-know 2016 conference, RSBDA'16, ACM, Graz, 2016

Konferenz
In this paper, we present work-in-progress on a recommender system designed to help people in need find the best suited social care institution for their personal issues. A key requirement in such a domain is to assure and to guarantee the person's privacy and anonymity in order to reduce inhibitions and to establish trust. We present how we aim to tackle this barely studied domain using a hybrid content-based recommendation approach. Our approach leverages three data sources containing textual content, namely (i) metadata from social care institutions, (ii) institution specific FAQs, and (iii) questions that a specific institution has already resolved. Additionally, our approach considers the time context of user questions as well as negative user feedback to previously provided recommendations. Finally, we demonstrate an application scenario of our recommender system in the form of a real-world Web system deployed in Austria.
2016

Rexha Andi, Dragoni Mauro, Kern Roman, Kröll Mark

An Information Retrieval Based Approach for Multilingual Ontology Matching

International Conference on Applications of Natural Language to Information Systems, Métais E., Meziane F., Saraee M., Sugumaran V., Vadera S. , Springer , Salford, UK, 2016

Konferenz
Ontology matching in a multilingual environment consists of finding alignments between ontologies modeled by using more than one language. Such a research topic combines traditional ontology matching algorithms with the use of multilingual resources, services, and capabilities for easing multilingual matching. In this paper, we present a multilingual ontology matching approach based on Information Retrieval (IR) techniques: ontologies are indexed through an inverted index algorithm and candidate matches are found by querying such indexes. We also exploit the hierarchical structure of the ontologies by adopting the PageRank algorithm for our system. The approaches have been evaluated using a set of domain-specific ontologies belonging to the agricultural and medical domain. We compare our results with existing systems following an evaluation strategy closely resembling a recommendation scenario. The version of our system using PageRank showed an increase in performance in our evaluations.
2016

Gursch Heimo, Ziak Hermann, Kröll Mark, Kern Roman

Context-Driven Federated Recommendations for Knowledge Workers

Proceedings of the 17th European Conference on Knowledge Management (ECKM), Dr. Sandra Moffett and Dr. Brendan Galbraith, Academic Conferences and Publishing International Limited, Belfast, Northern Ireland, UK, 2016

Konferenz
Modern knowledge workers need to interact with a large number of different knowledge sources with restricted or public access. Knowledge workers are thus burdened with the need to familiarise and query each source separately. The EEXCESS (Enhancing Europe’s eXchange in Cultural Educational and Scientific reSources) project aims at developing a recommender system providing relevant and novel content to its users. Based on the user’s work context, the EEXCESS system can either automatically recommend useful content, or support users by providing a single user interface for a variety of knowledge sources. In the design process of the EEXCESS system, recommendation quality, scalability and security where the three most important criteria. This paper investigates the scalability aspect achieved by federated design of the EEXCESS recommender system. This means that, content in different sources is not replicated but its management is done in each source individually. Recommendations are generated based on the context describing the knowledge worker’s information need. Each source offers result candidates which are merged and re-ranked into a single result list. This merging is done in a vector representation space to achieve high recommendation quality. To ensure security, user credentials can be set individually by each user for each source. Hence, access to the sources can be granted and revoked for each user and source individually. The scalable architecture of the EEXCESS system handles up to 100 requests querying up to 10 sources in parallel without notable performance deterioration. The re-ranking and merging of results have a smaller influence on the system's responsiveness than the average source response rates. The EEXCESS recommender system offers a common entry point for knowledge workers to a variety of different sources with only marginally lower response times as the individual sources on their own. Hence, familiarisation with individual sources and their query language is not necessary.
2016

Trattner Christoph, Kuśmierczyk Tomasz, Nørvåg Kjetil

FOODWEB - Studying Online Food Consumption and Production Patterns on the Web

ERCIM NEWS, ERCIM EEIG, 2016

Journal
2016

Kern Roman, Klampfl Stefan, Rexha Andi

Identifying Referenced Text in ScientificPublications by Summarisation andClassification Techniques

BIRNDL 2016 Joint Workshop on Bibliometric-enhanced Information Retrieval and NLP for Digital Libraries, G. Cabanac, Muthu Kumar Chandrasekaran, Ingo Frommholz , Kokil Jaidka, Min-Yen Kan, Philipp Mayr, Dietmar Wolfram, ACM, New Jersey, USA, 2016

Konferenz
This report describes our contribution to the 2nd ComputationalLinguistics Scientific Document Summarization Shared Task (CLSciSumm2016), which asked to identify the relevant text span in a referencepaper that corresponds to a citation in another document that citesthis paper. We developed three different approaches based on summarisationand classification techniques. First, we applied a modified versionof an unsupervised summarisation technique, TextSentenceRank, to thereference document, which incorporates the similarity of sentences tothe citation on a textual level. Second, we employed classification to selectfrom candidates previously extracted through the original TextSentenceRankalgorithm. Third, we used unsupervised summarisation of therelevant sub-part of the document that was previously selected in a supervisedmanner.
2016

Trattner Christoph, Oberegger Alexander, Eberhard Lukas, Parra Denis, Marinho Leandro

Understanding the Impact of Weather for POI recomennder systems

RecTour’16,, ACM, Boston, 2016

Konferenz
POI (point of interest) recommender systems for location-based social network services, such as Foursquare or Yelp,have gained tremendous popularity in the past few years.Much work has been dedicated into improving recommenda-tion services in such systems by integrating different featuresthat are assumed to have an impact on people’s preferencesfor POIs, such as time and geolocation. Yet, little atten-tion has been paid to the impact of weather on the users’final decision to visit a recommended POI. In this paper wecontribute to this area of research by presenting the firstresults of a study that aims to predict the POIs that userswill visit based on weather data. To this end, we extend thestate-of-the-art Rank-GeoFM POI recommender algorithmwith additional weather-related features, such as tempera-ture, cloud cover, humidity and precipitation intensity. Weshow that using weather data not only significantly increasesthe recommendation accuracy in comparison to the origi-nal algorithm, but also outperforms its time-based variant.Furthermore, we present the magnitude of impact of eachfeature on the recommendation quality, showing the need tostudy the weather context in more detail in the light of POIrecommendation systems.
2016

Pimas Oliver, Rexha Andi, Kröll Mark, Kern Roman

Profiling microblog authors using concreteness and sentiment - Know-Center at PAN 2016 author profiling

PAN 2016, Krisztian Balog, Linda Cappellato, Nicola Ferro, Craig Macdonald, Springer, Evora, Portugal, 2016

Konferenz
The PAN 2016 author profiling task is a supervised classification problemon cross-genre documents (tweets, blog and social media posts). Our systemmakes use of concreteness, sentiment and syntactic information present in thedocuments. We train a random forest model to identify gender and age of a document’sauthor. We report the evaluation results received by the shared task.
2016

Trattner Christoph, Kuśmierczyk Tomasz, Rokicki Markus, Herder Eelco

Plate and Prejudice: Gender Differences in Online Cooking

UMAP 2016, ACM, Halifax, NS, Canada , 2016

Konferenz
Historically, there have always been differences in how men andwomen cook or eat. The reasons for this gender divide have mostlygone in Western culture, but still there is qualitative and anecdotalevidence that men prefer heftier food, that women take care of everydaycooking, and that men cook to impress. In this paper, weshow that these differences can also quantitatively be observed in alarge dataset of almost 200 thousand members of an online recipecommunity. Further, we show that, using a set of 88 features, thegender of the cooks can be predicted with fairly good accuracy of75%, with preference for particular dishes, the use of spices andthe use of kitchen utensils being the strongest predictors. Finally,we show the positive impact of our results on online food reciperecommender systems that take gender information into account.
2016

Rexha Andi, Kröll Mark, Kern Roman

Social Media Monitoring for Companies: A 4W Summarisation Approach

European Conference on Knowledge Management, Dr. Sandra Moffett and Dr. Brendan Galbraith, Academic Conferences and Publishing International Limited, Belfast, Northern Ireland, UK, 2016

Konferenz
Monitoring (social) media represents one means for companies to gain access to knowledge about, for instance, competitors, products as well as markets. As a consequence, social media monitoring tools have been gaining attention to handle amounts of data nowadays generated in social media. These tools also include summarisation services. However, most summarisation algorithms tend to focus on (i) first and last sentences respectively or (ii) sentences containing keywords.In this work we approach the task of summarisation by extracting 4W (who, when, where, what) information from (social)media texts. Presenting 4W information allows for a more compact content representation than traditional summaries. Inaddition, we depart from mere named entity recognition (NER) techniques to answer these four question types by includingnon-rigid designators, i.e. expressions which do not refer to the same thing in all possible worlds such as “at the main square”or “leaders of political parties”. To do that, we employ dependency parsing to identify grammatical characteristics for each question type. Every sentence is then represented as a 4W block. We perform two different preliminary studies: selecting sentences that better summarise texts by achieving an F1-measure of 0.343, as well as a 4W block extraction for which we achieve F1-measures of 0.932; 0.900; 0.803; 0.861 for “who”, “when”, “where” and “what” category respectively. In a next step the 4W blocks are ranked by relevance. The top three ranked blocks, for example, then constitute a summary of the entire textual passage. The relevance metric can be customised to the user’s needs, for instance, ranked by up-to-dateness where the sentences’ tense is taken into account. In a user study we evaluate different ranking strategies including (i) up-todateness,(ii) text sentence rank, (iii) selecting the firsts and lasts sentences or (iv) coverage of named entities, i.e. based on the number of named entities in the sentence. Our 4W summarisation method presents a valuable addition to a company’s(social) media monitoring toolkit, thus supporting decision making processes.
2016

Yusuke Fukazawa, Kröll Mark, Strohmaier M., Ota Jun

IR based Task-Model Learning: Automating the hierarchical structuring of tasks

Web Intelligence, IOS Press, IOS Press, 2016

Journal
Task-models concretize general requests to support users in real-world scenarios. In this paper, we present an IR based algorithm (IRTML) to automate the construction of hierarchically structured task-models. In contrast to other approaches, our algorithm is capable of assigning general tasks closer to the top and specific tasks closer to the bottom. Connections between tasks are established by extending Turney’s PMI-IR measure. To evaluate our algorithm, we manually created a ground truth in the health-care domain consisting of 14 domains. We compared the IRTML algorithm to three state-of-the-art algorithms to generate hierarchical structures, i.e. BiSection K-means, Formal Concept Analysis and Bottom-Up Clustering. Our results show that IRTML achieves a 25.9% taxonomic overlap with the ground truth, a 32.0% improvement over the compared algorithms.
2015

Kowald Dominik

Modeling Cognitive Processes in Social Tagging to Improve Tag Recommendations

Proceedings of the 24th International Conference on World Wide Web Companion, WWW'2015, ACM, Florence, Italy, 2015

Konferenz
With the emergence of Web 2.0, tag recommenders have becomeimportant tools, which aim to support users in ndingdescriptive tags for their bookmarked resources. Althoughcurrent algorithms provide good results in terms of tag predictionaccuracy, they are often designed in a data-drivenway and thus, lack a thorough understanding of the cognitiveprocesses that play a role when people assign tags toresources. This thesis aims at modeling these cognitive dynamicsin social tagging in order to improve tag recommendationsand to better understand the underlying processes.As a rst attempt in this direction, we have implementedan interplay between individual micro-level (e.g., categorizingresources or temporal dynamics) and collective macrolevel(e.g., imitating other users' tags) processes in the formof a novel tag recommender algorithm. The preliminaryresults for datasets gathered from BibSonomy, CiteULikeand Delicious show that our proposed approach can outperformcurrent state-of-the-art algorithms, such as CollaborativeFiltering, FolkRank or Pairwise Interaction TensorFactorization. We conclude that recommender systems canbe improved by incorporating related principles of humancognition.
2015

Renner Bettina, Wesiak Gudrun, Cress, U.

Quantified self app usage tested in the workplace.

17th congress of the European Association of Work and Organizational Psychology (EAWOP), 2015

Konferenz
Purpose: This contribution relates the Quantified Self approach to computer supported workplace learning. It shows results of a large field study where 12 different apps where used in several work contexts. Design/Methodology: Participants used the apps during their work and during training sessions to track their behaviour and mood at work and capture problematic experiences. Data capturing was either automatically, e.g. tracking program usage on a computer, or by participants manually documenting their experiences. Users then reflected individually or collaboratively about their experiences. Results: Results show that participants liked the apps and used the opportunity to learn something from their work experiences. Users evaluated apps as useful for professional training and having long-term benefits when used in the work life. Computer supported reflection about own data and experiences seems to have especially potential where new processes happen, e.g. with unexperienced workers or in training settings. Limitations: Apps were used in the wild so control about potential external influencing factors is limited. Research/Practical Implications: Results show a successful application of apps supporting individual learning in the work life. This shows that the concept of Quantified Self is not limited to private life but also has chances to foster vocational development. Originality/Value: This contribution combines the pragmatic Quantified Self approach with the theoretical background of reflective learning. It presents data from a broad-based study of using such apps in real work life. The results of the study give insights about its potential in this area and about possible influencing factors and barriers.
2015

Mutlu Belgin, Sabol Vedran

Visual Analysis of Scientific Content

STCSN E-LETTER ON SCIENCE 2.0, 2015

Konferenz
The steadily increasing amount of scientific publications demands for more powerful, user-oriented technologiessupporting querying and analyzing scientific facts therein. Current digital libraries that provide services to accessscientific content are rather closed in a way that they deploy their own meta-models and technologies to query and analysethe knowledge contained in scientific publications. The goal of the research project CODE is to realize a framework basedon Linked Data principles which aims to provide methods for federated querying within scientific data, and interfacesenabling user to easily perform exploration and analysis tasks on received content. The main focus in this paper lieson the one hand on extraction and organization of scientific facts embedded in publications and on the other hand on anintelligent framework facilitating search and visual analysis of scientific facts through suggesting visualizations appropriatefor the underlying data.
2015

Wesiak Gudrun, Al-Smadi Mohammad, Gütl Christian, Höfler Margit

CSCL in non-technological environments: Evaluation of a Wiki system with integrated self- and peer assessment.

Proceedings of InPACT 2015 (International Psychological Applications Conference and Trends 2015), Ljubljana, Slovenia Cite this publication Gudrun Wesiak, 2015

Konferenz
Computer-supported collaborative learning (CSCL) is already a central element of online learningenvironments, but is also gaining increasing importance in traditional classroom settings where coursework is carried out in groups. For these situations social interaction, sharing and construction ofknowledge among the group members are important elements of the learning process. The use ofcomputers and the internet facilitates such group work by allowing asynchronous as well as synchronouscontributions toto foster CSCL is the employment of Wiki systems, e.g. for collaboratively working on a writingassignment. We developed an enhanced Wiki system with self- and peer assessment, visualizations, and-science students showed its usefulness for collaborative course work. However, results from studies withtech-savvy participants, who are typically familiar with the benefits as well as drawbacks of such tools,are often limited regarding the generalizability to other populations. Thus, we introduced the Wiki in anon-technological environment and evaluated it with respect to usability, usefulness, and motivationalcomponents. Thirty psychology students used the co-writing Wiki to work collaboratively on a shortpaper. Besides providing an interface for generating and changing a document, the co-writing Wiki offerstools for formative assessment activities (integrated self-, peer-, and group assessment activities) as well-data(activity tracking) as well as questionnaire data gathered at before and after working with the Wiki.Additionally, the instructor evaluated the co-writing Wiki concerning its usefulness for CSCL activities inacademic settings. Despite technical problems and consequently low system usability scores, participantsperceived the offered functionalities as helpful to keep a good overview on the current status of theirpaper and the contributions of their group members. The integrated self-assessment tool helped them toget aware of their strengths and weaknesses. In addition, students showed a high intrinsic motivationwhile working with the co-Writing Wiki, which did not change over the course of the study. From the-writing Wiki allowed to effectively monitor the progress of the groups andenabled formative feedback by the instructor. Summarizing, the results indicate that using Wikis forCSCL is a promising way to also support students with no technological background.environments, but is also gaining increasing importance in traditional classroom settings where coursework is carried out in groups. For these situations social interaction, sharing and construction ofknowledge among the group members are important elements of the learning process. The use ofcomputers and the internet facilitates such group work by allowing asynchronous as well as synchronous contributions to a common learning object independent of student’s working time and location. One way to foster CSCL is the employment of Wiki systems, e.g. for collaboratively working on a writing assignment. We developed an enhanced Wiki system with self- and peer assessment, visualizations, and functionalities for continuous teacher feedback. First evaluations of this ‘co-writing Wiki’ with computer science students showed its usefulness for collaborative course work. However, results from studies with tech-savvy participants, who are typically familiar with the benefits as well as drawbacks of such tools, are often limited regarding the generalizability to other populations. Thus, we introduced the Wiki in a non-technological environment and evaluated it with respect to usability, usefulness, and motivational components. Thirty psychology students used the co-writing Wiki to work collaboratively on a short paper. Besides providing an interface for generating and changing a document, the co-writing Wiki offers tools for formative assessment activities (integrated self-, peer-, and group assessment activities) as well as monitoring the progress of the group’s collaboration. The evaluation of the tool is based on log-data (activity tracking) as well as questionnaire data gathered at before and after working with the Wiki. Additionally, the instructor evaluated the co-writing Wiki concerning its usefulness for CSCL activities in academic settings. Despite technical problems and consequently low system usability scores, participants perceived the offered functionalities as helpful to keep a good overview on the current status of their paper and the contributions of their group members. The integrated self-assessment tool helped them to get aware of their strengths and weaknesses. In addition, students showed a high intrinsic motivation while working with the co-Writing Wiki, which did not change over the course of the study. From the instructor’s perspective, the co-writing Wiki allowed to effectively monitor the progress of the groups and enabled formative feedback by the instructor. Summarizing, the results indicate that using Wikis for CSCL is a promising way to also support students with no technological background.
2015

Kraker Peter, Enkhbayar Asuraa, Lex Elisabeth

Exploring Coverage and Distribution of Identifiers on the Scholarly Web

Proceedings of the 14th International Symposium of Information Science - ISI 2015, Zadar, Croatia, 2015

Konferenz
In a scientific publishing environment that is increasingly moving online,identifiers of scholarly work are gaining in importance. In this paper, weanalysed identifier distribution and coverage of articles from the discipline ofquantitative biology using arXiv, Mendeley and CrossRef as data sources.The results show that when retrieving arXiv articles from Mendeley, we wereable to find more papers using the DOI than the arXiv ID. This indicates thatDOI may be a better identifier with respect to findability. We also find thatcoverage of articles on Mendeley decreases in the most recent years, whereasthe coverage of DOIs does not decrease in the same order of magnitude. Thishints at the fact that there is a certain time lag involved, before articles arecovered in crowd-sourced services on the scholarly web.
2015

Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph

VizRec: Recommending Personalized Visualizations

ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Human Interaction with Artificial Advice Givers, ACM, 2015

Journal
Visualizations have a distinctive advantage when dealing with the information overload problem: since theyare grounded in basic visual cognition, many people understand them. However, creating the appropriaterepresentation requires specific expertise of the domain and underlying data. Our quest in this paper is tostudy methods to suggest appropriate visualizations autonomously. To be appropriate, a visualization hasto follow studied guidelines to find and distinguish patterns visually, and encode data therein. Thus, a visu-alization tells a story of the underlying data; yet, to be appropriate, it has to clearly represent those aspectsof the data the viewer is interested in. Which aspects of a visualization are important to the viewer? Canwe capture and use those aspects to recommend visualizations? This paper investigates strategies to recom-mend visualizations considering different aspects of user preferences. A multi-dimensional scale is used toestimate aspects of quality for charts for collaborative filtering. Alternatively, tag vectors describing chartsare used to recommend potentially interesting charts based on content. Finally, a hybrid approach combinesinformation on what a chart is about (tags) and how good it is (ratings). We present the design principlesbehindVizRec, our visual recommender. We describe its architecture, the data acquisition approach with acrowd sourced study, and the analysis of strategies for visualization recommendation
2015

Kraker Peter, Schlögl Christian, Jack Kris, Lindstaedt Stefanie

The Quest for Keeping an Overview: Knowledge Domain Visualizations based on Co-Readership Patterns

2015

Given the enormous amount of scientific knowledge that is produced each and every day, the need for better ways of gaining–and keeping–an overview of research fields is becoming more and more apparent. In a recent paper published in the Journal of Informetrics [1], we analyze the adequacy and applicability of readership statistics recorded in social reference management systems for creating such overviews. First, we investigated the distribution of subject areas in user libraries of educational technology researchers on Mendeley. The results show that around 69% of the publications in an average user library can be attributed to a single subject area. Then, we used co-readership patterns to map the field of educational technology. The resulting knowledge domain visualization, based on the most read publications in this field on Mendeley, reveals 13 topic areas of educational technology research. The visualization is a recent representation of the field: 80% of the publications included were published within ten years of data collection. The characteristics of the readers, however, introduce certain biases to the visualization. Knowledge domain visualizations based on readership statistics are therefore multifaceted and timely, but it is important that the characteristics of the underlying sample are made transparent.
2015

Kraker Peter, Schlögl C. , Jack K., Lindstaedt Stefanie

The Quest for Keeping an Overview: Knowledge Domain Visualizations based on Co-Readership Patterns

In: Science 2.0, IEEE Computer Society Special Technical Community on Social Networking E-Letter, vol. 3, no. 1, 2015

Journal
Given the enormous amount of scientific knowledgethat is produced each and every day, the need for better waysof gaining – and keeping – an overview of research fields isbecoming more and more apparent. In a recent paper publishedin the Journal of Informetrics [1], we analyze the adequacy andapplicability of readership statistics recorded in social referencemanagement systems for creating such overviews. First, weinvestigated the distribution of subject areas in user librariesof educational technology researchers on Mendeley. The resultsshow that around 69% of the publications in an average userlibrary can be attributed to a single subject area. Then, we usedco-readership patterns to map the field of educational technology.The resulting knowledge domain visualization, based on the mostread publications in this field on Mendeley, reveals 13 topicareas of educational technology research. The visualization isa recent representation of the field: 80% of the publicationsincluded were published within ten years of data collection. Thecharacteristics of the readers, however, introduce certain biasesto the visualization. Knowledge domain visualizations based onreadership statistics are therefore multifaceted and timely, but itis important that the characteristics of the underlying sample aremade transparent.
2015

Vignoli Michela, Kraker Peter, Sevault A.

Paving the way for Science 2.0: top-down and bottom-up approaches

International Conference for E-Democracy and Open Government (CEDEM'15), Krems, Austria, 2015

Konferenz
Science 2.0 is the current trend towards using Web 2.0 tools in research and practising a more open science. We are currently at the beginning of a transition phase in which traditional structures, processes, value systems, and means of science communication are being put to the proof. New strategies and models under the label of “open” are being explored and partly implemented. This situation implies a number of insecurities for scientists as well as for policy makers and demands a rethinking and overcoming of some habits and conventions persisting since an era before the internet. This paper lists current barriers to practising Open Science from the point of view of researchers and reflects which measures could help overcoming them. The central question is which initiatives should be taken on institutional or political level and which ones on level of the community or the individual scientist to support the transition to Science 2.0.
2015

Autorenschaftserkennung für Bibliometrie unter Verwendung stylometrischer Attribute

Mining Scientific Papers: Computational Linguistics and Bibliometrics, 2015

The overwhelming majority of scientific publications are authored by multiple persons; yet, bibliographic metrics are only assigned to individual articles as single entities. In this paper, we aim at a more fine-grained analysis of scientific authorship. We therefore adapt a text segmentation algorithm to identify potential author changes within the main text of a scientific article, which we obtain by using existing PDF extraction techniques. To capture stylistic changes in the text, we employ a number of stylometric features. We evaluate our approach on a small subset of PubMed articles consisting of an approximately equal number of research articles written by a varying number of authors. Our results indicate that the more authors an article has the more potential author changes are identified. These results can be considered as an initial step towards a more detailed analysis of scientific authorship, thereby extending the repertoire of bibliometrics.
2015

Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context

MSM-MUSE PostProceedings, Springer, 2015

Journal
In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory. Based on a theory of human memory, the approach estimates a tag's probability being applied by a particular user as a function of usage frequency and recency of the tag in the user's past. This probability is further refined by considering the in uence of the current semantic context of the user's tagging situation. Using three real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike and Flickr, we show how refining frequency-based estimates by considering usage recency and contextual in uence outperforms conventional "most popular tags" approaches and another existing and very effective but less theory-driven, time-dependent recommendation mechanism. By combining our approach with a simple resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as FolkRank, Pairwise Interaction Tensor Factorization and Collaborative Filtering. We conclude that our approach provides an accurate and computationally efficient model of a user's temporal tagging behavior. We demonstrate how effective principles of recommender systems can be designed and implemented if human memory processes are taken into account.
2015

The Social Semantic Server: A Flexible Framework to Support Informal Learning at the Workplace

ACM, In Proceeding of 15th International Conference on Knowledge Technologies and Data-driven Business, 2015

Konferenz
Informal learning at the workplace includes a multitude of processes. Respective activities can be categorized into multiple perspectives on informal learning, such as reflection, sensemaking, help seeking and maturing of collective knowledge. Each perspective raises requirements with respect to the technical support, this is why an integrated solution relying on social, adaptive and semantic technologies is needed. In this paper, we present the Social Semantic Server, an extensible, open-source application server that equips client-side tools with services to support and scale informal learning at the workplace. More specifically, the Social Semantic Server semantically enriches social data that is created at the workplace in the context of user-to-user or user-artifact interactions. This enriched data can then in turn be exploited in informal learning scenarios to, e.g., foster help seeking by recommending collaborators, resources, or experts. Following the design-based research paradigm, the Social Semantic Server has been implemented based on design principles, which were derived from theories such as Distributed Cognition and Meaning Making. We illustrate the applicability and efficacy of the Social Semantic Server in the light of three real-world applications that have been developed using its social semantic services. Furthermore, we report preliminary results of two user studies that have been carried out recently.
2015

Kraker Peter, Lindstaedt Stefanie , Schlögl C., Jack K.

Visualization of co-readership patterns from an online reference management system

Journal of Informetrics, Elsevier, NULL, 2015

Journal
In this paper, we analyze the adequacy and applicability of readership statistics recorded in social reference management systems for creating knowledge domain visualizations. First, we investigate the distribution of subject areas in user libraries of educational technology researchers on Mendeley. The results show that around 69% of the publications in an average user library can be attributed to a single subject area. Then, we use co-readership patterns to map the field of educational technology. The resulting visualization prototype, based on the most read publications in this field on Mendeley, reveals 13 topic areas of educational technology research. The visualization is a recent representation of the field: 80% of the publications included were published within ten years of data collection. The characteristics of the readers, however, introduce certain biases to the visualization. Knowledge domain visualizations based on readership statistics are therefore multifaceted and timely, but it is important that the characteristics of the underlying sample are made transparent.
2015

Simon Jörg Peter, Pammer-Schindler Viktoria, Schmidt Peter

An Energy Efficient Implementation of Differential Synchronization on Mobile Devices

Proceedings of 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) , London, 2015

Konferenz
Synchronisation algorithms are central components of collab- orative editing software. The energy efficiency for such algo- rithms becomes of interest to a wide community of mobile application developers. In this paper we explore the differen- tial synchronisation (diffsync) algorithm with respect to en- ergy consumption on mobile devices.We identify three areas for optimisation: a.) Empty cycles where diffsync is executed although no changes need to be processed b.) tail energy by adapting cycle intervals and c.) computational complexity. We propose a push-based diffsync strategy in which synchronisation cycles are triggered when a device connects to the network or when a device is notified of changes. Discussions within this paper are based on real usage data of PDF annotations via the Mendeley iOS app.
2015

Kowald Dominik, Kopeinik S., Seitlinger Paul, Trattner Christoph, Ley Tobias

Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context

Mining, Modeling, and Recommending'Things' in Social Media, MSM'2015, Springer, 2015

Buch
In this paper, we introduce a tag recommendation algorithmthat mimics the way humans draw on items in their long-term memory.Based on a theory of human memory, the approach estimates a tag'sprobability being applied by a particular user as a function of usagefrequency and recency of the tag in the user's past. This probability isfurther refined by considering the inuence of the current semantic contextof the user's tagging situation. Using three real-world folksonomiesgathered from bookmarks in BibSonomy, CiteULike and Flickr, we showhow refining frequency-based estimates by considering usage recency andcontextual inuence outperforms conventional "most popular tags" approachesand another existing and very effective but less theory-driven,time-dependent recommendation mechanism.By combining our approach with a simple resource-specific frequencyanalysis, our algorithm outperforms other well-established algorithms,such as FolkRank, Pairwise Interaction Tensor Factorization and CollaborativeFiltering. We conclude that our approach provides an accurateand computationally efficient model of a user's temporal tagging behavior.We demonstrate how effective principles of recommender systemscan be designed and implemented if human memory processes are takeninto account.
2015

Kowald Dominik, Seitlinger Paul, Kopeinik Simone, Ley Tobias, Trattner Christoph

Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender

Mining, Modeling, and Recommending'Things' in Social Media, MSM'2015, Springer, 2015

Buch
We assume that recommender systems are more successful,when they are based on a thorough understanding of how people processinformation. In the current paper we test this assumption in the contextof social tagging systems. Cognitive research on how people assign tagshas shown that they draw on two interconnected levels of knowledge intheir memory: on a conceptual level of semantic fields or LDA topics,and on a lexical level that turns patterns on the semantic level intowords. Another strand of tagging research reveals a strong impact oftime-dependent forgetting on users' tag choices, such that recently usedtags have a higher probability being reused than "older" tags. In thispaper, we align both strands by implementing a computational theory ofhuman memory that integrates the two-level conception and the processof forgetting in form of a tag recommender. Furthermore, we test theapproach in three large-scale social tagging datasets that are drawn fromBibSonomy, CiteULike and Flickr.As expected, our results reveal a selective effect of time: forgetting ismuch more pronounced on the lexical level of tags. Second, an extensiveevaluation based on this observation shows that a tag recommender interconnectingthe semantic and lexical level based on a theory of humancategorization and integrating time-dependent forgetting on the lexicallevel results in high accuracy predictions and outperforms other wellestablishedalgorithms, such as Collaborative Filtering, Pairwise InteractionTensor Factorization, FolkRank and two alternative time-dependentapproaches. We conclude that tag recommenders will benefit from goingbeyond the manifest level of word co-occurrences, and from includingforgetting processes on the lexical level.
2015

Lacic Emanuel, Kowald Dominik, Eberhard Lukas, Trattner Christoph, Parra Denis, Marinho Leandro

Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces

Mining, Modeling, and Recommending'Things' in Social Media, MSM'2015, Springer, 2015

Buch
Recent research has unveiled the importance of online social networks for improving the quality of recommender systems and encouraged the research community to investigate better ways of exploiting the social information for recommendations. To contribute to this sparse field of research, in this paper we exploit users’ interactions along three data sources (marketplace, social network and location-based) to assess their performance in a barely studied domain: recommending products and domains of interests (i.e., product categories) to people in an online marketplace environment. To that end we defined sets of content- and network-based user similarity features for each data source and studied them isolated using an user-based Collaborative Filtering (CF) approach and in combination via a hybrid recommender algorithm, to assess which one provides the best recommendation performance. Interestingly, in our experiments conducted on a rich dataset collected from SecondLife, a popular online virtual world, we found that recommenders relying on user similarity features obtained from the social network data clearly yielded the best results in terms of accuracy in case of predicting products, whereas the features obtained from the marketplace and location-based data sources also obtained very good results in case of predicting categories. This finding indicates that all three types of data sources are important and should be taken into account depending on the level of specialization of the recommendation task.
2015

Seitlinger Paul, Kowald Dominik, Kopeinik Simone, Hasani-Mavriqi Ilire, Ley Tobias, Lex Elisabeth

Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

In 24rd International World Wide Web Conference, Web-Science Track, Aldo Gangemi, Stefano Leonardi and Alessandro Panconesi, ACM, Florence, 2015

Konferenz
Classic resource recommenders like Collaborative Filtering(CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and inter-pretation. In this paper, we propose a novel hybrid rec-ommendation strategy that re nes CF by capturing thesedynamics. The evaluation results reveal that our approachsubstantially improves CF and, depending on the dataset,successfully competes with a computationally much moreexpensive Matrix Factorization variant.
2015

Veas Eduardo Enrique, Mutlu Belgin, di Sciascio Maria Cecilia, Tschinkel Gerwald, Sabol Vedran

Visual Recommendations for Scientific and Cultural Content

IVAPP 2015, Berlin, 2015

Konferenz
Supporting individuals who lack experience or competence to evaluate an overwhelming amout of informationsuch as from cultural, scientific and educational content makes recommender system invaluable to cope withthe information overload problem. However, even recommended information scales up and users still needto consider large number of items. Visualization takes a foreground role, letting the user explore possiblyinteresting results. It leverages the high bandwidth of the human visual system to convey massive amounts ofinformation. This paper argues the need to automate the creation of visualizations for unstructured data adaptingit to the user’s preferences. We describe a prototype solution, taking a radical approach considering bothgrounded visual perception guidelines and personalized recommendations to suggest the proper visualization.
2015

Kraker Peter, Lex Elisabeth, Gorraiz Juan, Gumpenberger Christian, Peters Isabella

Research Data Explored II: the Anatomy and Reception of figshare

Proceedings of the 20th International Conference on Science and Technology Indicators (STI 2015), Lugano, Schweiz, 2015

Konferenz
2015

Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph, Sabol Vedran

VizRec: A Two-Stage Recommender System for Personalized Visualizations

ACM IUI, ACM, Atlanta, Georgia, USA, 2015

Konferenz
Identifying and using the information from distributed and heterogeneous information sources is a challenging task in many application fields. Even with services that offer welldefined structured content, such as digital libraries, it becomes increasingly difficult for a user to find the desired information. To cope with an overloaded information space, we propose a novel approach – VizRec– combining recommender systems (RS) and visualizations. VizRec suggests personalized visual representations for recommended data. One important aspect of our contribution and a prerequisite for VizRec are user preferences that build a personalization model. We present a crowd based evaluation and show how such a model of preferences can be elicited.
2015

Buschmann Katrin, Kasberger Stefan, Mayer Katja, Reckling Falk, Rieck Katharina, Vignoli Michela, Kraker Peter

Open Science in Austria: Approaches and Status

Information. Wissenschaft und Praxis, DeGruyter, 2015

Journal
Insbesondere in den letzten zwei Jahren hat Österreichim Bereich Open Science, vor allem was Open Accessund Open Data betrifft, nennenswerte Fortschritte gemacht.Die Gründung des Open Access Networks Austria(OANA) und das Anfang 2014 gestartete Projekt e-InfrastructuresAustria können als wichtige Grundsteine fürden Ausbau einer österreichischen Open-Science-Landschaftgesehen werden. Auch das österreichische Kapitelder Open Knowledge Foundation leistet in den BereichenOpen Science Praxis- und Bewusstseinsbildung grundlegendeArbeit. Unter anderem bilden diese Initiativendie Grundlage für den Aufbau einer nationalen Open-Access-Strategie sowie einer ganz Österreich abdeckendenInfrastruktur für Open Access und Open (Research) Data.Dieser Beitrag gibt einen Überblick über diese und ähnlichenationale sowie lokale Open-Science-Projekte und-Initiativen und einen Ausblick in die mögliche Zukunftvon Open Science in Österreich.
2015

Die Informationsflut überblicken: Visualisierung von Wissensdomänen auf Basis von Co-Readership

IEEE STCSN-E-Letter, 2015

Konferenz
2015

ScaR: Towards a Real-Time Recommender Framework Following the Microservices Architecture

ACM, 9th ACM Conference on Recommender Systems, 2015

Various recommender frameworks have been proposed, but still there is a lack of work that addresses important aspects like: immediately considering streaming data within the recommendation process; scalability of the recommender system; real-time recommendation based on different context dependent data. To bridge these gaps, we contribute with a novel recommender framework and show how different context dependent data sources can be supported within a real-world scenario.
2015

Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems

ACM, 9th ACM Conference on Recommender Systems, 2015

In this paper, we present work-in-progress on a recommender system based on Collaborative Filtering that exploits location information gathered by indoor positioning systems. This approach allows us to provide recommendations for "extreme" cold-start users with absolutely no item interaction data available, where methods based on Matrix Factorization would not work. We simulate and evaluate our proposed system using data from the location-based FourSquare system and show that we can provide substantially better recommender accuracy results than a simple MostPopular baseline that is typically used when no interaction data is available.
2015

Effiziente Suchergebnisdiversifikation durch Anfrageerweiterung auf Basis von Wissensdatenbanken

Proceedings of 12th International Workshop on Text-based Information Retrieval (TIR), 2015

Underspecified search queries can be performed via result list diversification approaches, which are often computationally complex and require longer response times. In this paper, we explore an alternative, and more efficient way to diversify the result list based on query expansion. To that end, we used a knowledge base pseudo-relevance feedback algorithm. We compared our algorithm to IA-Select, a state-of-the-art diversification method, using its intent-aware version of the NDCG (Normalized Discounted Cumulative Gain) metric. The results indicate that our approach can guarantee a similar extent of diversification as IA-Select. In addition, we showed that the supported query language of the underlying search engines plays an important role in the query expansion based on diversification. Therefore, query expansion may be an alternative when result diversification is not feasible, for example in federated search systems where latency and the quantity of handled search results are critical issues.
2015

Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender

MSM-MUSE PostProceedings, Springer, 2015

Journal
We assume that recommender systems are more successful, when they are based on a thorough understanding of how people process information. In the current paper we test this assumption in the context of social tagging systems. Cognitive research on how people assign tags has shown that they draw on two interconnected levels of knowledge in their memory: on a conceptual level of semantic fields or LDA topics, and on a lexical level that turns patterns on the semantic level into words. Another strand of tagging research reveals a strong impact of time-dependent forgetting on users' tag choices, such that recently used tags have a higher probability being reused than "older" tags. In this paper, we align both strands by implementing a computational theory of human memory that integrates the two-level conception and the process of forgetting in form of a tag recommender. Furthermore, we test the approach in three large-scale social tagging datasets that are drawn from BibSonomy, CiteULike and Flickr. As expected, our results reveal a selective effect of time: forgetting is much more pronounced on the lexical level of tags. Second, an extensive evaluation based on this observation shows that a tag recommender interconnecting the semantic and lexical level based on a theory of human categorization and integrating time-dependent forgetting on the lexical level results in high accuracy predictions and outperforms other wellestablished algorithms, such as Collaborative Filtering, Pairwise Interaction Tensor Factorization, FolkRank and two alternative time-dependent approaches. We conclude that tag recommenders will benefit from going beyond the manifest level of word co-occurrences, and from including forgetting processes on the lexical level.
2015

Vereinheitlichter Wissenszugang für Wissensarbeiter durch einen föderierten Empfehlungsdienst

Mensch und Computer 2015 – Workshopband, Anette Weisbecker, Michael Burmester, Albrecht Schmidt, De Gruyter Oldenbourg, 2015

The objective of the EEXCESS (Enhancing Europe’s eXchange in Cultural Educational and Scientific reSources) project is to develop a system that can automatically recommend helpful and novel content to knowledge workers. The EEXCESS system can be integrated into existing software user interfaces as plugins which will extract topics and suggest the relevant material automatically. This recommendation process simplifies the information gathering of knowledge workers. Recommendations can also be triggered manually via web frontends. EEXCESS hides the potentially large number of knowledge sources by semi or fully automatically providing content suggestions. Hence, users only have to be able to in use the EEXCESS system and not all sources individually. For each user, relevant sources can be set or auto-selected individually. EEXCESS offers open interfaces, making it easy to connect additional sources and user program plugins.
2015

Explorieren von zeitlichen Zusammenhängen in semantischen Graphen

Proceedings of SIGRAD 2015, Visualization and Interactive Graphics in Computer Games and Education, June 1-2, 2015, Stockholm, Sweden, 2015

The analysis of temporal relationships in large amounts of graph data has gained significance in recent years. Information providers such as journalists seek to bring order into their daily work when dealing with temporally distributed events and the network of entities, such as persons, organisations or locations, which are related to these events. In this paper we introduce a time-oriented graph visualisation approach which maps temporal information to visual properties such as size, transparency and position and, combined with advanced graph navigation features, facilitates the identification and exploration of temporal relationships. To evaluate our visualisation, we compiled a dataset of ~120.000 news articles from international press agencies including Reuters, CNN, Spiegel and Aljazeera. Results from an early pilot study show the potentials of our visualisation approach and its usefulness for nalysing temporal relationships in large data sets.
2015

Warum eine off ene Wissenschaft eine bürgerfreundliche Wissenschaft ist

oead.news, OeAD (Österreichische Austauschdienst), 2015

Konferenz
2015

A Critical Look at the ResearchGate Score as a Measure of Scientific Reputation

Quantifying and Analysing Scholarly Communication on the Web (ASCW’15 at ACM Web Science), 2015

2015

The amount of information available on the inter- net and within enterprises has reached an incredible dimension. Efficiently finding and understanding information and thereby saving resources remains one of the major challenges in our daily work. Powerfu

Proceedings of the 19th International Conference on Information Visualisation (IV2015), 2015

The amount of information available on the internet and within enterprises has reached an incredible dimension. Efficiently finding and understanding information and thereby saving resources remains one of the major challenges in our daily work. Powerful text analysis methods, a scalable faceted retrieval engine and a well-designed interactive user interface are required to address the problem. Besides providing means for drilling-down to the relevant piece of information, a part of the challenge arises from the need of analysing and visualising data to discover relationships and correlations, gain an overview of data distributions and unveil trends. Visual interfaces leverage the enormous bandwidth of the human visual system to support pattern discovery in large amounts of data. Our Knowminer search builds upon the well-known faceted search approach which is extended with interactive visualisations allowing users to analyse different aspects of the result set. Additionally, our system provides functionality for organising interesting search results into portfolios, and also supports social features for rating and boosting search results and for sharing and annotating portfolios.
2015

Horn Christopher, Kern Roman

Deriving Public Transportation Timetables with Large-Scale Cell Phone Data

Procedia Computer Science, 2015

Konferenz
In this paper, we propose an approach to deriving public transportation timetables of a region (i.e. country) based on (i) large- scale, non-GPS cell phone data and (ii) a dataset containing geographic information of public transportation stations. The presented algorithm is designed to work with movements data, which are scarce and have a low spatial accuracy but exists in vast amounts (large-scale). Since only aggregated statistics are used, our algorithm copes well with anonymized data. Our evaluation shows that 89% of the departure times of popular train connections are correctly recalled with an allowed deviation of 5 minutes. The timetable can be used as feature for transportation mode detection to separate public from private transport when no public timetable is available.
2015

Offener Datensatz für Fallerkennung basierend auf Sensordaten mobiler Endgeräte

12th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2015

Journal
Fall detection is a classical use case for mobile phone sensing. Nonetheless, no open dataset exists that could be used to train, test and compare fall detection algorithms. We present a dataset for mobile phone sensing-based fall detection. The dataset contains both accelerometer and gyroscope data. Data were labelled with four types of falls (e.g., “stumbling”) and ten types of non-fall activities (e.g., “sit down”). The dataset was collected with martial artists who simulated falls. We used five different state-of-the-art Android smartphone models worn on the hip in a small bag. Due to the datasets properties of using multiple devices and being labelled with multiple fall- and non-fall categories, we argue that it is suitable to serve as benchmark dataset.
2015

Modeling Cognitive Processes in Social Tagging to Improve Tag Recommendations

International World Wide Web Conference Committee (IW3C2), ACM, 2015

With the emergence of Web 2.0, tag recommenders have become important tools, which aim to support users in nding descriptive tags for their bookmarked resources. Although current algorithms provide good results in terms of tag prediction accuracy, they are often designed in a data-driven way and thus, lack a thorough understanding of the cognitive processes that play a role when people assign tags to resources. This thesis aims at modeling these cognitive dynamics in social tagging in order to improve tag recommendations and to better understand the underlying processes. As a rst attempt in this direction, we have implemented an interplay between individual micro-level (e.g., categorizing resources or temporal dynamics) and collective macrolevel (e.g., imitating other users' tags) processes in the form of a novel tag recommender algorithm. The preliminary results for datasets gathered from BibSonomy, CiteULike and Delicious show that our proposed approach can outperform current state-of-the-art algorithms, such as Collaborative Filtering, FolkRank or Pairwise Interaction Tensor Factorization. We conclude that recommender systems can be improved by incorporating related principles of human cognition.
2015

Herleitung von oeffentlichen Fahrplaenen mittels Mobilfunkdaten

Procedia Computer Science, 2015

In this paper, we propose an approach to deriving public transportation timetables of a region (i.e. country) based on (i) large- scale, non-GPS cell phone data and (ii) a dataset containing geographic information of public transportation stations. The presented algorithm is designed to work with movements data, which are scarce and have a low spatial accuracy but exists in vast amounts (large-scale). Since only aggregated statistics are used, our algorithm copes well with anonymized data. Our evaluation shows that 89% of the departure times of popular train connections are correctly recalled with an allowed deviation of 5 minutes. The timetable can be used as feature for transportation mode detection to separate public from private transport when no public timetable is available.
2015

Open Science in Österreich: Ansätze und Status

Information. Wissenschaft und Praxis, DeGruyter, 2015

Konferenz
2015

Assoziierung von Intention und Emotion in Weblogs

International Conference on Applications of Natural Language to Information Systems, 2015

People willingly provide more and more information about themselves on social media platforms. This personal information about users’ emotions (sentiment) or goals (intent) is particularly valuable, for instance, for monitoring tools. So far, sentiment and intent analysis were conducted separately. Yet, both aspects can complement each other thereby informing processes such as explanation and reasoning. In this paper, we investigate the relation between intent and sentiment in weblogs. We therefore extract ~90,000 human goal instances from the ICWSM 2009 Spinn3r dataset and assign respective sentiments. Our results indicate that associating intent with sentiment represents a valuable addition to research areas such as text analytics and text understanding.
2015

Simon Jörg Peter, Schmidt Peter, Pammer-Schindler Viktoria

Eine energieeffiziente Implementierung von Differential Synchronization auf mobilen Endgeräten

Proceedings of 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2015

Synchronisation algorithms are central components of collab- orative editing software. The energy efficiency for such algo- rithms becomes of interest to a wide community of mobile application developers. In this paper we explore the differen- tial synchronisation (diffsync) algorithm with respect to en- ergy consumption on mobile devices. We identify three areas for optimisation: a.) Empty cycles where diffsync is executed although no changes need to be processed b.) tail energy by adapting cycle intervals and c.) computational complexity. We propose a push-based diffsync strategy in which synchronisation cycles are triggered when a device connects to the network or when a device is notified of changes. Discussions within this paper are based on real usage data of PDF annotations via the Mendeley iOS app.
2015

Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces

MSM-MUSE PostProceedings, Springer, 2015

Journal
Recent research has unveiled the importance of online social networks for improving the quality of recommender systems and encouraged the research community to investigate better ways of exploiting the social information for recommendations. To contribute to this sparse field of research, in this paper we exploit users’ interactions along three data sources (marketplace, social network and location-based) to assess their performance in a barely studied domain: recommending products and domains of interests (i.e., product categories) to people in an online marketplace environment. To that end we defined sets of content- and network-based user similarity features for each data source and studied them isolated using an user-based Collaborative Filtering (CF) approach and in combination via a hybrid recommender algorithm, to assess which one provides the best recommendation performance. Interestingly, in our experiments conducted on a rich dataset collected from SecondLife, a popular online virtual world, we found that recommenders relying on user similarity features obtained from the social network data clearly yielded the best results in terms of accuracy in case of predicting products, whereas the features obtained from the marketplace and location-based data sources also obtained very good results in case of predicting categories. This finding indicates that all three types of data sources are important and should be taken into account depending on the level of specialization of the recommendation task.
2015

Lex Elisabeth, Dennerlein Sebastian

HowTo: Scientific Work in Interdisciplinary and Distributed Teams

In: Science 2.0, IEEE Computer Society Special Technical Community on Social Networking E-Letter, vol. 3, no. 1, IEEE, 2015

Journal
Today's complex scientific problems often require interdisciplinary, team-oriented approaches: the expertise of researchers from different disciplines is needed to collaboratively reach a solution. Interdisciplinary teams yet face many challenges such as differences in research practice, terminology, communication , and in the usage of tools. In this paper, we therefore study concrete mechanisms and tools of two real-world scientific projects with the aim to examine their efficacy and influence on interdisciplinary teamwork. For our study, we draw upon Bronstein's model of interdisciplinary collaboration. We found that it is key to use suitable environments for communication and collaboration, especially when teams are geographically distributed. Plus, the willingness to share (domain) knowledge is not a given and requires strong common goals and incentives. Besides, structural barriers such as financial aspects can hinder interdisciplinary work, especially in applied, industry funded research. Furthermore, we observed a kind of cold-start problem in interdisciplinary collaboration, when there is no work history and when the disciplines are rather different, e.g. in terms of wording. HowTo: Scientific Work in Interdisciplinary and Distributed Teams (PDF Download Available). Available from: https://www.researchgate.net/publication/282813815_HowTo_Scientific_Work_in_Interdisciplinary_and_Distributed_Teams [accessed Jul 13, 2017].
2015

Kern Roman, Frey Matthias

Efficient Table Annotation for Digital Articles

4th International Workshop on Mining Scientific Publications, D-Lib, 2015

Konferenz
Table recognition and table extraction are important tasks in information extraction, especially in the domain of schol- arly communication. In this domain tables are commonplace and contain valuable information. Many different automatic approaches for table recognition and extraction exist. Com- mon to many of these approaches is the need for ground truth datasets, to train algorithms or to evaluate the results. In this paper we present the PDF Table Annotator, a web based tool for annotating elements and regions in PDF doc- uments, in particular tables. The annotated data is intended to serve as a ground truth useful to machine learning algo- rithms for detecting table regions and table structure. To make the task of manual table annotation as convenient as possible, the tool is designed to allow an efficient annotation process that may spawn multiple session by multiple users. An evaluation is conducted where we compare our tool to three alternative ways of creating ground truth of tables in documents. Here we found that our tool overall provides an efficient and convenient way to annotate tables. In addition, our tool is particularly suitable for complex table structures, where it provided the lowest annotation time and the highest accuracy. Furthermore, our tool allows to annotate tables following a logical or a functional model. Given that by the use of our tool ground truth datasets for table recognition and extraction are easier to produce, the quality of auto- matic tables extraction should greatly benefit. General
2015

Klampfl Stefan, Kern Roman

Machine Learning Techniques for Automatically Extracting Contextual Information from Scientific Publications

Semantic Web Evaluation Challenges. SemWebEval 2015 at ESWC 2015, Portorož, Slovenia, May 31 – June 4, 2015, Revised Selected Papers, Gandon, F.; Cabrio, E.; Stankovic, M.; Zimmermann, A. , Springer International Publishing, 2015

Konferenz
Scholarly publishing increasingly requires automated systems that semantically enrich documents in order to support management and quality assessment of scientific output.However, contextual information, such as the authors' affiliations, references, and funding agencies, is typically hidden within PDF files.To access this information we have developed a processing pipeline that analyses the structure of a PDF document incorporating a diverse set of machine learning techniques.First, unsupervised learning is used to extract contiguous text blocks from the raw character stream as the basic logical units of the article.Next, supervised learning is employed to classify blocks into different meta-data categories, including authors and affiliations.Then, a set of heuristics are applied to detect the reference section at the end of the paper and segment it into individual reference strings.Sequence classification is then utilised to categorise the tokens of individual references to obtain information such as the journal and the year of the reference.Finally, we make use of named entity recognition techniques to extract references to research grants, funding agencies, and EU projects.Our system is modular in nature.Some parts rely on models learnt on training data, and the overall performance scales with the quality of these data sets.
2015

Ruiz-Calleja Adolfo, Dennerlein Sebastian, Tomberg Vladimir , Pata Kai, Ley Tobias, Theiler Dieter, Lex Elisabeth

Supporting learning analytics for informal workplace learning with a social semantic infrastructure

In Proceedings of the European Conference on Technology Enhanced Learning, Springer International Publishing (in press)., Springer, Toledo, Spain, 2015

Konferenz
This paper presents the potential of a social semantic infrastructure that implements an Actor Artifact Network (AAN) with the final goal of supporting learning analytics at the workplace. Two applications were built on top of such infrastructure and make use of the emerging relations of such a AAN. A preliminary evaluation shows that an AAN can be created out of the usage of both applications, thus opening the possibility to implement learning analytics at the workplace.
2015

Lindstaedt Stefanie , Ley Tobias, Sack Harald

Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business

i-KNOW '15 15th International Conference on Knowledge Technologies and Data-Driven Business, 2015

Buch
2015

Fessl Angela, Feyertag Sandra, Pammer-Schindler Viktoria

Designing Innovative Digital Technologies for Knowledge Management and Data-driven Business: A Case Study

In Proceeding of 15th International Conference on Knowledge Technologies and Data-driven Business, 2015

Konferenz
This paper presents a case study on co-designing digitaltechnologies for knowledge management and data-driven businessfor an SME. The goal of the case study was to analysethe status quo of technology usage and to develop designsuggestions in form of mock-ups tailored to the company’sneeds. We used both requirements engineering and interactivesystem design methods such as interviews, workshops,and mock-ups for work analysis and system design. The casestudy illustrates step-by-step the processes of knowledge extractionand combination (analysis) and innovation creation(design). These processes resulted in non-functional mockups,which are planned to be implemented within the SME.
2015

Parra Denis, Gomez M., Hutardo D., Wen X., Lin Yu-Ru, Trattner Christoph

Twitter in academic events: {A} study of temporal usage, communication, and topical patterns in 16 Computer Science conferences

Computer Communications, Elsevier, 2015

Journal
Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physicalsetting, on-site and off-site attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter.In this paper we analyze scholars’ Twitter usage in 16 Computer Science conferences over a timespan of five years. Ourprimary finding is that over the years there are differences with respect to the uses of Twitter, with an increase ofinformational activity (retweets and URLs), and a decrease of conversational usage (replies and mentions), which alsoimpacts the network structure – meaning the amount of connected components – of the informational and conversationalnetworks. We also applied topic modeling over the tweets’ content and found that when clustering conferences accordingto their topics the resulting dendrogram clearly reveals the similarities and differences of the actual research interests ofthose events. Furthermore, we also analyzed the sentiment of tweets and found persistent differences among conferences.It also shows that some communities consistently express messages with higher levels of emotions while others do it in amore neutral manner. Finally, we investigated some features that can help predict future user participation in the onlineTwitter conference activity. By casting the problem as a classification task, we created a model that identifies factors thatcontribute to the continuing user participation. Our results have implications for research communities to implementstrategies for continuous and active participation among members. Moreover, our work reveals the potential for the useof information shared on Twitter in order to facilitate communication and cooperation among research communities, byproviding visibility to new resources or researchers from relevant but often little known research communities.
2015

Kröll Mark, Strohmaier M.

Associating Intent with Sentiment in Weblogs

International Conference on Applications of Natural Language to Information Systems, NLDB'15, Springer-Verlag, Passau, Germany, 2015

Konferenz
People willingly provide more and more information about themselves on social media platforms. This personal information about users’ emotions (sentiment) or goals (intent) is particularly valuable, for instance, for monitoring tools. So far, sentiment and intent analysis were conducted separately. Yet, both aspects can complement each other thereby informing processes such as explanation and reasoning. In this paper, we investigate the relation between intent and sentiment in weblogs. We therefore extract ~90,000 human goal instances from the ICWSM 2009 Spinn3r dataset and assign respective sentiments. Our results indicate that associating intent with sentiment represents a valuable addition to research areas such as text analytics and text understanding.
2015

Lacic Emanuel, Luzhnica Granit, Simon Jörg Peter, Traub Matthias, Lex Elisabeth, Kowald Dominik

Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems

Proceedings of 9th International Conference on Recommender Systems, RecSys'2015, ACM, Vienna, Austria, 2015

Konferenz
In this paper, we present work-in-progress on a recommender system based on Collaborative Filtering that exploits location information gathered by indoor positioning systems. This approach allows us to provide recommendations for "extreme" cold-start users with absolutely no item interaction data available, where methods based on Matrix Factorization would not work. We simulate and evaluate our proposed system using data from the location-based FourSquare system and show that we can provide substantially better recommender accuracy results than a simple MostPopular baseline that is typically used when no interaction data is available.
2015

Larrain Santiago, Parra Denis, Graells-Garrido Eduardo, Nørvåg Kjetil, Trattner Christoph

Good Times Bad Times: A Study on Recency Effects in Collaborative Filtering for Social Tagging

Proceedings of the 9th {ACM} Conference on Recommender Systems, ACM, 2015

Konferenz
In this paper, we present work-in-progress of a recently startedproject that aims at studying the effect of time in recommendersystems in the context of social tagging. Despite the existence ofprevious work in this area, no research has yet made an extensiveevaluation and comparison of time-aware recommendation methods.With this motivation, this paper presents results of a studywhere we focused on understanding (i) “when” to use the temporalinformation into traditional collaborative filtering (CF) algorithms,and (ii) “how” to weight the similarity between users and itemsby exploring the effect of different time-decay functions. As theresults of our extensive evaluation conducted over five social taggingsystems (Delicious, BibSonomy, CiteULike, MovieLens, andLast.fm) suggest, the step (when) in which time is incorporated inthe CF algorithm has substantial effect on accuracy, and the typeof time-decay function (how) plays a role on accuracy and coveragemostly under pre-filtering on user-based CF, while item-basedshows stronger stability over the experimental conditions.
2015

Lacic Emanuel, Traub Matthias, Kowald Dominik, Lex Elisabeth

ScaR: Towards a Real-Time Recommender Framework Following the Microservices Architecture

In the Large-Scale Recommender Systems Workshop (LSRS'15) at the 9th International Conference on Recommender Systems, RecSys'2015, ACM, Vienna, Austria, 2015

Konferenz
In this paper, we present our approach towards an effective scalable recommender framework termed ScaR. Our framework is based on the microservices architecture and exploits search technology to provide real-time recommendations. Since it is our aim to create a system that can be used in a broad range of scenarios, we designed it to be capable of handling various data streams and sources. We show its efficacy and scalability with an initial experiment on how the framework can be used in a large-scale setting.
2015

Dennerlein Sebastian, Treasure-Jones Tamsin, Tomberg Vladimir, Theiler Dieter, Lex Elisabeth, Ley Tobias

Making Sense of Informal Learning at the Workplace

AMEE - Conference (The Association for Medical Education in Europe), Glasgow, UK, 2015

Konferenz
Sensemaking at the workplace and in educational contexts has been extensively studied for decades. Interestingly, making sense out of the own wealth of learning experiences at the workplace has been widely ignored. To tackle this issue, we have implemented a novel sensemaking interface for healthcare professionals to support learning at the workplace. The proposed prototype supports remembering of informal experiences from episodic memory followed by sensemaking in semantic memory. Results from an initial study conducted as part of an iterative co-design process reveal the prototype is being perceived as useful and supportive for informal sensemaking by study participants from the healthcare domain. Furthermore, we find first evidence that re-evaluation of collected information is a potentially necessary process that needs further exploration to fully understand and support sensemaking of informal learning experiences.
2015

Trattner Christoph, Parra Denis, Brusilovsky Peter, Marinho Leandro

Report on the SIGIR 2015 Workshop on Social Personalization and Search

SIGIR FORUM, ACM, 2015

Konferenz
The use of contexts –side information associated to information tasks– has been one ofthe most important dimensions for the improvement of Information Retrieval tasks, helpingto clarify the information needs of the users which usually start from a few keywords in atext box. Particularly, the social context has been leveraged in search and personalizationsince the inception of the Social Web, but even today we find new scenarios of informationfiltering, search, recommendation and personalization where the use of social signals canproduce a steep improvement. In addition, the action of searching has become a social processon the Web, making traditional assumptions of relevance obsolete and requiring newparadigms for matching the most useful resources that solve information needs. This escenariohas motivated us for organizing the Social Personalization and Search (SPS) workshop,a forum aimed at sharing and discussing research that leverage social data for improvingclassic personalization models for information access and to revisiting search from individualphenomena to a collaborative process.
2015

Dennerlein Sebastian, Kaiser René, Barreiros Carla, Gutounig Robert , Rauter Romana

Knowledge Strategies in Organisations – a Case for the Barcamp Format

Proceedings of the 16th European Conference on Knowledge Management, ACPI, Udine, Italy, 2015

Konferenz
Barcamps are events for open knowledge exchange. They are generally open to everyone, irrespective of background or discipline, and request no attendance fee. Barcamps are structured by only a small set of common rules and invite participants to an interactive and interdisciplinary discourse on an equal footing. In contrast to scientific conferences, the program is decided by the participants themselves on-site. Barcamps are often called un-conferences or ad-hoc conferences. Since barcamps are typically attended by people in their spare time, their motivation to actively engage and benefit from participating is very high. This paper presents a case study conducted at the annual Barcamp Graz in Austria. Within the case study, two field studies (quantitative and qualitative) and a parallel participant observation were carried out between 2010 and 2014. In these investigations we elaborated on the differences of the barcamp to scientific conferences, inferred characteristics of barcamps for knowledge generation, sharing and transfer in organizations and propose three usages of barcamps in organizations: further education of employees, internal knowledge transfer and getting outside knowledge in. Barcamps can be used as further education for employees enabling not only knowledge sharing, generation and transfer via the participating employees, but also for informally promoting a company’s competences. With respect to internal knowledge transfer, hierarchical boundaries can be temporarily broken by allowing informal and interactive discussion. This can lead to the elicitation of ‘hidden’ knowledge, knowledge transfer resulting in more efficient teamwork and interdepartmental cooperation. Finally, external stakeholders such as customers and partners can be included in this process to get outside knowledge in and identify customer needs, sketch first solutions and to start concrete projects. As a result of the case study, we hypothesise as a step towards further research that organisations can benefit from utilising this format as knowledge strategy.
2015

Trattner Christoph, Steurer Michael

Detecting partnership in location-based and online social networks

Social Netw. Analys. Mining, Springer, 2015

Journal
Existing approaches to identify the tie strength between users involve typically only one type of network. To date, no studies exist that investigate the intensity of social relations and in particular partnership between users across social networks. To fill this gap in the literature, we studied over 50 social proximity features to detect the tie strength of users defined as partnership in two different types of networks: location-based and online social networks. We compared user pairs in terms of partners and non-partners and found significant differences between those users. Following these observations, we evaluated the social proximity of users via supervised and unsupervised learning approaches and establish that location-based social networks have a great potential for the identification of a partner relationship. In particular, we established that location-based social networks and correspondingly induced features based on events attended by users could identify partnership with 0.922 AUC, while online social network data had a classification power of 0.892 AUC. When utilizing data from both types of networks, a partnership could be identified to a great extent with 0.946 AUC. This article is relevant for engineers, researchers and teachers who are interested in social network analysis and mining.
2015

Traub Matthias, Kowald Dominik, Lacic Emanuel, Lex Elisabeth, Schoen Pepjin, Supp Gernot

Smart booking without looking: providing hotel recommendations in the TripRebel portal

Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business, i-know 2015, ACM, Graz, Austria, 2015

Konferenz
In this paper, we present a scalable hotel recommender system for TripRebel, a new online booking portal. On the basis of the open-source enterprise search platform Apache Solr, we developed a system architecture with Web-based services to interact with indexed data at large scale as well as to provide hotel recommendations using various state-of-the-art recommender algorithms. We demonstrate the efficiency of our system directly using the live TripRebel portal where, in its current state, hotel alternatives for a given hotel are calculated based on data gathered from the Expedia AffiliateNetwork (EAN).
2015

Wozelka Ralph, Kröll Mark, Sabol Vedran

Exploring Time Relations in Semantic Graphs

Proceedings of SIGRAD, SIGRAD, Linköping University Electronic Press, Stockholm, Sweden, 2015

Konferenz
The analysis of temporal relationships in large amounts of graph data has gained significance in recent years. In-formation providers such as journalists seek to bring order into their daily work when dealing with temporally dis-tributed events and the network of entities, such as persons, organisations or locations, which are related to these events. In this paper we introduce a time-oriented graph visualisation approach which maps temporal information to visual properties such as size, transparency and position and, combined with advanced graph navigation features, facilitates the identification and exploration of temporal relationships. To evaluate our visualisation, we compiled a dataset of ~120.000 news articles from international press agencies including Reuters, CNN, Spiegel and Aljazeera. Results from an early pilot study show the potentials of our visualisation approach and its usefulness for analysing temporal relationships in large data sets.
2015

Scherer Reinhold, Schwarz Andreas , Müller-Putz G. R. , Pammer-Schindler Viktoria, Lloria Garcia Mariano

Game-based BCI training: Interactive design for individuals with cerebral palsy

IEEE International Conference on Systems, Man, Cybernetics 2015, 2015

Konferenz
Mutual brain-machine co-adaptation is the mostcommon approach used to gain control over spontaneouselectroencephalogram (EEG) based brain-computer interfaces(BCIs). Co-adaptation means the concurrent or alternating useof machine learning and the brain’s reinforcement learningmechanisms. Results from the literature, however, suggest thatcurrent implementations of this approach does not lead todesired results (“BCI inefficiency”). In this paper, we proposean alternative strategy that implements some recommendationsfrom educational psychology and instructional design. We presenta jigsaw puzzle game for Android devices developed to train theBCI skill in individuals with cerebral palsy (CP). Preliminaryresults of a supporting study in four CP users suggest high useracceptance. Three out of the four users achieved better thanchance accuracy in arranging pieces to form the puzzle.Index Terms—Brain-Computer Interface, Electroencephalo-gram, Human-Computer Interaction, Game-based learning,Cerebral palsy.
2015

di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique

uRank: Exploring Document Recommendations through an Interactive User-Driven Approach

RecSys Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'15), CEUR-WS, 2015

Konferenz
Whenever we gather or organize knowledge, the task of searching inevitably takes precedence. As exploration unfolds, it becomes cumbersome to reorganize resources along new interests, as any new search brings new results. Despite huge advances in retrieval and recommender systems from the algorithmic point of view, many real-world interfaces have remained largely unchanged: results appear in an infinite list ordered by relevance with respect to the current query. We introduce uRank, a user-driven visual tool for exploration and discovery of textual document recommendations. It includes a view summarizing the content of the recommendation set, combined with interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. We provide a formal experiment showing that uRank users can browse the document collection and efficiently gather items relevant to particular topics of interest with significantly lower cognitive load compared to traditional list-based representations.
2015

Kraker Peter

Educational Technology as Seen Through the Eyes of the Readers

International Journal of Technology Enhanced Learning, Inderscience Publishers, 2015

Journal
In this paper, I present the evaluation of a novel knowledge domain visualization of educational technology. The interactive visualization is based on readership patterns in the online reference management system Mendeley. It comprises of 13 topic areas, spanning psychological, pedagogical, and methodological foundations, learning methods and technologies, and social and technological developments. The visualization was evaluated with (1) a qualitative comparison to knowledge domain visualizations based on citations, and (2) expert interviews. The results show that the co-readership visualization is a recent representation of pedagogical and psychological research in educational technology. Furthermore, the co-readership analysis covers more areas than comparable visualizations based on co-citation patterns. Areas related to computer science, however, are missing from the co-readership visualization and more research is needed to explore the interpretations of size and placement of research areas on the map.
2015

Cook John, Ley Tobias, Maier Ronald, Mor Yishay, Santos Patricia, Lex Elisabeth, Dennerlein Sebastian, Trattner Christoph, Holley Debbie

Using the Hybrid Social Learning Network to Explore Concepts, Practices, Designs and Smart Services for Networked Professional Learning

In Proceedings of the International Conference on Smart Learning Environments 2015 (ICSLE 2015), Springer, Sinaia, Romania, 2015

Konferenz
In this paper we define the notion of the Hybrid Social Learning Network. We propose mechanisms for interlinking and enhancing both the practice of professional learning and theories on informal learning. Our approach shows how we employ empirical and design work and a participatory pattern workshop to move from (kernel) theories via Design Principles and prototypes to social machines articulating the notion of a HSLN. We illustrate this approach with the example of Help Seeking for healthcare professionals.
2015

Rubien Raoul, Ziak Hermann, Kern Roman

Efficient Search Result Diversification via Query Expansion Using Knowledge Bases

Proceedings of 12th International Workshop on Text-based Information Retrieval (TIR), 2015

Konferenz
Underspecified search queries can be performed via result list diversification approaches, which are often compu- tationally complex and require longer response times. In this paper, we explore an alternative, and more efficient way to diversify the result list based on query expansion. To that end, we used a knowledge base pseudo-relevance feedback algorithm. We compared our algorithm to IA-Select, a state-of-the-art diversification method, using its intent-aware version of the NDCG (Normalized Discounted Cumulative Gain) metric. The results indicate that our approach can guarantee a similar extent of diversification as IA-Select. In addition, we showed that the supported query language of the underlying search engines plays an important role in the query expansion based on diversification. Therefore, query expansion may be an alternative when result diversification is not feasible, for example in federated search systems where latency and the quantity of handled search results are critical issues.
2015

Pujari Subhash Chandra, Hadgu Asmelah Teka, Lex Elisabeth, Jäschke Robert

Social Activity versus Academic Activity: A Case Study of Computer Scientists on Twitter

In Proceedings of the 15th International Conference on Knowledge Technologies and Data-Driven Business (i-KNOW 2015), ACM, Graz, Austria, 2015

Konferenz
In this work, we study social and academic network activities of researchers from Computer Science. Using a recently proposed framework, we map the researchers to their Twitter accounts and link them to their publications. This enables us to create two types of networks: first, networks that reflect social activities on Twitter, namely the researchers’ follow, retweet and mention networks and second, networks that reflect academic activities, that is the co-authorship and citation networks. Based on these datasets, we (i) compare the social activities of researchers with their academic activities, (ii) investigate the consistency and similarity of communities within the social and academic activity networks, and (iii) investigate the information flow between different areas of Computer Science in and between both types of networks. Our findings show that if co-authors interact on Twitter, their relationship is reciprocal, increasing with the numbers of papers they co-authored. In general, the social and the academic activities are not correlated. In terms of community analysis, we found that the three social activity networks are most consistent with each other, with the highest consistency between the retweet and mention network. A study of information flow revealed that in the follow network, researchers from Data Management, HumanComputer Interaction, and Artificial Intelligence act as a source of information for other areas in Computer Science.
2015

Tatzgern Markus, Grasset Raphael, Veas Eduardo Enrique, Schmalstieg Dieter

Exploring real world points of interest: Design and evaluation of object-centric exploration techniques for augmented reality

Pervasive and Mobile Computing, Elsevier, 2015

Journal
Augmented reality (AR) enables users to retrieve additional information about real world objects and locations. Exploring such location-based information in AR requires physical movement to different viewpoints, which may be tiring and even infeasible when viewpoints are out of reach. In this paper, we present object-centric exploration techniques for handheld AR that allow users to access information freely using a virtual copy metaphor. We focus on the design of techniques that allow the exploration of large real world objects. We evaluated our interfaces in a series of studies in controlled conditions and compared them to a 3D map interface, which is a more common method for accessing location-based information. Based on our findings, we put forward design recommendations that should be considered by future generations of location-based AR browsers, 3D tourist guides or situated urban planning.
2015

Ruiz-Calleja Adolfo, Dennerlein Sebastian, Tomberg Vladimir , Ley Tobias , Theiler Dieter, Lex Elisabeth

Integrating data across workplace learning applications with a social semantic infrastructure

Proceedings of the International Conference on Web-based Learning, Springer International Publishing, Hong Kong, China, 2015

Konferenz
This paper presents our experiences using a social semantic infrastructure that implements a semantically-enriched Actor Artifact Network (AAN) to support informal learning at the workplace. Our previous research led us to define the Model of Scaling Informal Learning, to identify several common practices when learning happens at the workplace, and to propose a social semantic infrastructure able to support them. This paper shows this support by means of two illustrative examples where practitioners employed several applications integrated into the infrastructure. Thus, this paper clarifies how workplace learning processes can be supported with such infrastructure according to the aforementioned model. The initial analysis of these experiences gives promising results since it shows how the infrastructure mediates in the sharing of contextualized learning artifacts and how it builds up an AAN that makes explicit the relationships between actors and artifacts when learning at the workplace.
2015

Hasani-Mavriqi Ilire, Geigl Florian, Pujari Subhash Chandra, Lex Elisabeth, Helic Denis

Influence of Status Social on Consensus Building in Collaboration Networks

In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2015), Jian Pei, Fabrizio Silvestri and Jie Tang, ACM/IEEE, Paris, France, 2015

Konferenz
In this paper, we analyze the influence of socialstatus on opinion dynamics and consensus building in collaborationnetworks. To that end, we simulate the diffusion of opinionsin empirical collaboration networks by taking into account boththe network structure and the individual differences of peoplereflected through their social status. For our simulations, weadapt a well-known Naming Game model and extend it withthe Probabilistic Meeting Rule to account for the social statusof individuals participating in a meeting. This mechanism issufficiently flexible and allows us to model various situations incollaboration networks, such as the emergence or disappearanceof social classes. In this work, we concentrate on studyingthree well-known forms of class society: egalitarian, ranked andstratified. In particular, we are interested in the way these societyforms facilitate opinion diffusion. Our experimental findingsreveal that (i) opinion dynamics in collaboration networks isindeed affected by the individuals’ social status and (ii) thiseffect is intricate and non-obvious. In particular, although thesocial status favors consensus building, relying on it too stronglycan slow down the opinion diffusion, indicating that there is aspecific setting for each collaboration network in which socialstatus optimally benefits the consensus building process.
2015

Kravcik Milos, Mikroyannidis Alexander, Pammer-Schindler Viktoria, Prilla Michael , Ullmann T.D.

Proceedings of the 5th Workshop on Awareness and Reflection in Technology Enhanced Learning.  In conjunction with the 10th European Conference on Technology Enhanced Learning: Design for Teaching and Learning in a Networked World

ARTEL 2015 Awareness and Reflection in Technology Enhanced Learning , 2015

Buch
2015

Dennerlein Sebastian, Kowald Dominik, Lex Elisabeth, Lacic Emanuel, Theiler Dieter, Ley Tobias

The Social Semantic Server: A Flexible Framework to Support Informal Learning at the Workplace

In Proceedings of the 15th International Conference on Knowledge Technologies and Data-Driven Business, i-know 2015, ACM, Graz, Austria, 2015

Konferenz
Informal learning at the workplace includes a multitude of processes. Respective activities can be categorized into multiple perspectives on informal learning, such as reflection, sensemaking, help seeking and maturing of collective knowledge. Each perspective raises requirements with respect to the technical support, this is why an integrated solution relying on social, adaptive and semantic technologies is needed. In this paper, we present the Social Semantic Server, an extensible, open-source application server that equips clientside tools with services to support and scale informal learning at the workplace. More specifically, the Social Semantic Server semantically enriches social data that is created at the workplace in the context of user-to-user or user-artifact interactions. This enriched data can then in turn be exploited in informal learning scenarios to, e.g., foster help seeking by recommending collaborators, resources, or experts. Following the design-based research paradigm, the Social Semantic Server has been implemented based on design principles, which were derived from theories such as Distributed Cognition and Meaning Making. We illustrate the applicability and efficacy of the Social Semantic Server in the light of three real-world applications that have been developed using its social semantic services. Furthermore, we report preliminary results of two user studies that have been carried out recently.
2015

Fessl Angela, Wesiak Gudrun, Feyertag Sandra, Rivera-Pelayo Verónica

In-App Reflection Guidance for Workplace Learning

Proceedings of the 10th European Conference on Technoloy Enhanced Learning (ECTEL 2015), Springer, NULL, 2015

Konferenz
In-app reflection guidance for workplace learning means motivating and guiding users to reflect on their working and learning, based on users' activities captured by the app. In this paper, we present ageneric concept for such in-app reflection guidance for workplace learning, its implementation in three di erent applications, and its evaluation in three di erent settings (one setting per app). From this experience, we draw the following lessons learned: First, the implemented in-appreflection guidance components are perceived as useful tools for reflective learning and their usefulness increases with higher usage rates. Second, smart technological support is su fficient to trigger reflection, however with di fferent implemented components also reflective learning takesplace on di erent stages. A sophisticated, unobtrusive integration in the working environment is not trivial at all. Automatically created prompts need a sensible timing in order to be perceived as useful and must not disrupt the current working processes.
2015

Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph, Sabol Vedran

Towards a Recommender Engine for Personalized Visualizations

UMAP, 2015

Konferenz
isualizations have a distinctive advantage when dealing with the information overload problem: being grounded in basic visual cognition, many people understand visualizations. However, when it comes to creating them, it requires specific expertise of the domain and underlying data to determine the right representation. Although there are rules that help generate them, the results are too broad as these methods hardly account for varying user preferences. To tackle this issue, we propose a novel recommender system that suggests visualizations based on (i) a set of visual cognition rules and (ii) user preferences collected in Amazon-Mechanical Turk. The main contribution of this paper is the introduction and the evaluation of a novel approach called VizRec that is able suggest an optimal list of top-n visualizations for heterogeneous data sources in a personalized manner.
2015

Peters Isabella, Kraker Peter, Lex Elisabeth, Gumpenberger Christian, Gorraiz, Juan

Research Data Explored: Citations versus Altmetrics

15th International Conference on Scientometrics and Informetrics, Online, 2015

Konferenz
The study explores the citedness of research data, its distribution over time and how it is related to the availability of a DOI (Digital Object Identifier) in Thomson Reuters' DCI (Data Citation Index). We investigate if cited research data "impact" the (social) web, reflected by altmetrics scores, and if there is any relationship between the number of citations and the sum of altmetrics scores from various social media-platforms. Three tools are used to collect and compare altmetrics scores, i.e. PlumX, ImpactStory, and Altmetric.com. In terms of coverage, PlumX is the most helpful altmetrics tool. While research data remain mostly uncited (about 85%), there has been a growing trend in citing data sets published since 2007. Surprisingly, the percentage of the number of cited research data with a DOI in DCI has decreased in the last years. Only nine repositories account for research data with DOIs and two or more citations. The number of cited research data with altmetrics scores is even lower (4 to 9%) but shows a higher coverage of research data from the last decade. However, no correlation between the number of citations and the total number of altmetrics scores is observable. Certain data types (i.e. survey, aggregate data, and sequence data) are more often cited and receive higher altmetrics scores.
2015

Rexha Andi, Klampfl Stefan, Kröll Mark, Kern Roman

Towards Authorship Attribution for Bibliometrics using Stylometric Features

Proc. of the Workshop Mining Scientific Papers: Computational Linguistics and Bibliometrics, Atanassova, I.; Bertin, M.; Mayr, P., ACL Anthology, Istanbul, Turkey, 2015

Konferenz
The overwhelming majority of scientific publications are authored by multiple persons; yet, bibliographic metrics are only assigned to individual articles as single entities. In this paper, we aim at a more fine-grained analysis of scientific authorship. We therefore adapt a text segmentation algorithm to identify potential author changes within the main text of a scientific article, which we obtain by using existing PDF extraction techniques. To capture stylistic changes in the text, we employ a number of stylometric features. We evaluate our approach on a small subset of PubMed articles consisting of an approximately equal number of research articles written by a varying number of authors. Our results indicate that the more authors an article has the more potential author changes are identified. These results can be considered as an initial step towards a more detailed analysis of scientific authorship, thereby extending the repertoire of bibliometrics.
2015

Veas Eduardo Enrique, Sabol Vedran, Singh Santokh, Ulbrich Eva Pauline

Reading through Graphics: Interactive Landscapes to Explore Dynamic Topic Spaces

Proceedings Part I of the 17th HCI International Conference, HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015, 2015

Konferenz
An information landscape is commonly used to represent relatedness in large, high-dimensional datasets, such as text document collections. In this paper we present interactive metaphors, inspired in map reading and visual transitions, that enhance the landscape representation for the analysis of topical changes in dynamic text repositories. The goal of interactive visualizations is to elicit insight, to allow users to visually formulate hypotheses about the underlying data and to prove them. We present a user study that investigates how users can elicit information about topics in a large document set. Our study concentrated on building and testing hypotheses using the map reading metaphors. The results show that people indeed relate topics in the document set from spatial relationships shown in the landscape, and capture the changes to topics aided by map reading metaphors.
2015

Tschinkel Gerwald, di Sciascio Maria Cecilia, Mutlu Belgin, Sabol Vedran

The Recommendation Dashboard: A System to Visualise and Organise Recommendations

Proceedings of the 19th International Conference on Information Visualisation (IV2015), 2015

Konferenz
Recommender systems are becoming common tools supportingautomatic, context-based retrieval of resources.When the number of retrieved resources grows large visualtools are required that leverage the capacity of humanvision to analyse large amounts of information. Weintroduce a Web-based visual tool for exploring and organisingrecommendations retrieved from multiple sourcesalong dimensions relevant to cultural heritage and educationalcontext. Our tool provides several views supportingfiltering in the result set and integrates a bookmarkingsystem for organising relevant resources into topic collections.Building upon these features we envision a systemwhich derives user’s interests from performed actions anduses this information to support the recommendation process.We also report on results of the performed usabilityevaluation and derive directions for further development.
2015

Rauch Manuela, Klieber Hans-Werner, Wozelka Ralph, Singh Santokh, Sabol Vedran

Knowminer Search - a Multi-Visualisation Collaborative Approach to Search Result Analysis

Proceedings of the 19th International Conference on Information Visualisation (IV2015), 2015

Konferenz
The amount of information available on the internet and within enterprises has reached an incredible dimension.Efficiently finding and understanding information and thereby saving resources remains one of the major challenges in our daily work. Powerful text analysis methods, a scalable faceted retrieval engine and a well-designed interactive user interface are required to address the problem. Besides providing means for drilling-down to the relevant piece of information, a part of the challenge arises from the need of analysing and visualising data to discover relationships and correlations, gain an overview of data distributions and unveil trends. Visual interfaces leverage the enormous bandwidth of the human visual system to support pattern discovery in large amounts of data. Our Knowminer search builds upon the well-known faceted search approach which is extended with interactive visualisations allowing users to analyse different aspects of the result set. Additionally, our system provides functionality for organising interesting search results into portfolios, and also supports social features for rating and boosting search results and for sharing and annotating portfolios.
2015

Wertner Alfred, Czech Paul, Pammer-Schindler Viktoria

An Open Labelled Dataset for Mobile Phone Sensing Based Fall Detection

12th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2015

Konferenz
Fall detection is a classical use case for mobile phone sensing.Nonetheless, no open dataset exists that could be used totrain, test and compare fall detection algorithms.We present a dataset for mobile phone sensing-based fall detection.The dataset contains both accelerometer and gyroscopedata. Data were labelled with four types of falls(e.g., “stumbling”) and ten types of non-fall activities (e.g.,“sit down”). The dataset was collected with martial artistswho simulated falls. We used five different state-of-the-artAndroid smartphone models worn on the hip in a small bag.Due to the datasets properties of using multiple devices andbeing labelled with multiple fall- and non-fall categories, weargue that it is suitable to serve as benchmark dataset.
2015

Dennerlein Sebastian, Theiler Dieter, Marton Peter, Lindstaedt Stefanie , Lex Elisabeth, Santos Patricia, Cook John

KnowBrain: An Online Social Knowledge Repository for Informal Workplace Learning

In Proceedings of the European Conference on Technology Enhanced Learning, Springer International Publishing (in press)., Springer, Toledo, Spain, 2015

Konferenz
We present KnowBrain (KB), an open source Dropbox-like knowledge repository with social features for informal workplace learning. KB enables users (i) to share and collaboratively structure knowledge, (ii) to access knowledge via sophisticated content- and metadatabased search and recommendation, and (iii) to discuss artefacts by means of multimedia-enriched Q&A. As such, KB can support, integrate and foster various collaborative learning processes related to daily work-tasks.
2015

Pammer-Schindler Viktoria, Bratic Marina, Feyertag Sandra, Faltin Nils

The Value of Self-tracking and the Added Value of Coaching in the Case of Improving Time Management

Proceedings of the10th European Conference on Technology Enhanced Learning (ECTEL 2015), 2015

Konferenz
We report two 6-week studies, each with 10 participants, on improving time management. In each study a different interventions was administered, in parallel to otherwise regular work: In the self-tracking setting, participants used only an activity logging tool to track their time use and a reflective practice, namely daily review of time use, to improve time management. In the coaching setting, participants did the same, but additionally received weekly bilateral coaching. In both settings, participants reported learning about time management. This is encouraging, as such self-directed learning is clearly cheaper than coaching. Only participants in the coaching setting however improved their self-assessment with respect to predefined time management best practices. The Value of Self-tracking and the Added Value of Coaching in the Case of Improving Time Management. Available from: https://www.researchgate.net/publication/300259607_The_Value_of_Self-tracking_and_the_Added_Value_of_Coaching_in_the_Case_of_Improving_Time_Management [accessed Oct 24 2017].
2015

Ziak Hermann, Kern Roman

Evaluation of Pseudo Relevance Feedback Techniques for Cross Vertical Aggregated Search

6th International Conference of the CLEF Association, CLEF'15, Toulouse, France, September 8-11, 2015, Proceedings, Springer, 2015

Konferenz
Cross vertical aggregated search is a special form of meta search, were multiple search engines from different domains and varying behaviour are combined to produce a single search result for each query. Such a setting poses a number of challenges, among them the question of how to best evaluate the quality of the aggregated search results. We devised an evaluation strategy together with an evaluation platform in order to conduct a series of experiments. In particular, we are interested whether pseudo relevance feedback helps in such a scenario. Therefore we implemented a number of pseudo relevance feedback techniques based on knowledge bases, where the knowledge base is either Wikipedia or a combination of the underlying search engines themselves. While conducting the evaluations we gathered a number of qualitative and quantitative results and gained insights on how different users compare the quality of search result lists. In regard to the pseudo relevance feedback we found that using Wikipedia as knowledge base generally provides a benefit, unless for entity centric queries, which are targeting single persons or organisations. Our results will enable to help steering the development of cross vertical aggregated search engines and will also help to guide large scale evaluation strategies, for example using crowd sourcing techniques.
2015

Schulze Gunnar, Horn Christopher, Kern Roman

Map-Matching Cell Phone Trajectories of Low Spatial and Temporal Accuracy

2015 IEEE 18th International Conference on Intelligent Transportation Systems, IEEE, IEEE, 2015

Konferenz
This paper presents an approach for matching cell phone trajectories of low spatial and temporal accuracy to the underlying road network. In this setting, only the position of the base station involved in a signaling event and the timestamp are known, resulting in a possible error of several kilometers. No additional information, such as signal strength, is available. The proposed solution restricts the set of admissible routes to a corridor by estimating the area within which a user is allowed to travel. The size and shape of this corridor can be controlled by various parameters to suit different requirements. The computed area is then used to select road segments from an underlying road network, for instance OpenStreetMap. These segments are assembled into a search graph, which additionally takes the chronological order of observations into account. A modified Dijkstra algorithm is applied for finding admissible candidate routes, from which the best one is chosen. We performed a detailed evaluation of 2249 trajectories with an average sampling time of 260 seconds. Our results show that, in urban areas, on average more than 44% of each trajectory are matched correctly. In rural and mixed areas, this value increases to more than 55%. Moreover, an in-depth evaluation was carried out to determine the optimal values for the tunable parameters and their effects on the accuracy, matching ratio and execution time. The proposed matching algorithm facilitates the use of large volumes of cell phone data in Intelligent Transportation Systems, in which accurate trajectories are desirable.
2015

Lin Yi-ling, Trattner Christoph, Brusilovsky Peter , He Daqing

The impact of image descriptions on user tagging behavior: A study of the nature and functionality of crowdsourced tags

JASIST, Wiley, 2015

Journal
Crowdsourcing has been emerging to harvest social wisdom from thousands of volunteers to perform series of tasks online. However, little research has been devoted to exploring the impact of various factors such as the content of a resource or crowdsourcing interface design to user tagging behavior. While images’ titles and descriptions are frequently available in image digital libraries, it is not clear whether they should be displayed to crowdworkers engaged in tagging. This paper focuses on offering an insight to the curators of digital image libraries who face this dilemma by examining (i) how descriptions influence the user in his/her tagging behavior and (ii) how this relates to the (a) nature of the tags, (b) the emergent folksonomy, and (c) the findability of the images in the tagging system. We compared two different methods for collecting image tags from Amazon’s Mechanical Turk’s crowdworkers – with and without image descriptions. Several properties of generated tags were examined from different perspectives: diversity, specificity, reusability, quality, similarity, descriptiveness, etc. In addition, the study was carried out to examine the impact of image description on supporting users’ information seeking with a tag cloud interface. The results showed that the properties of tags are affected by the crowdsourcing approach. Tags from the “with description” condition are more diverse and more specific than tags from the “without description” condition, while the latter has a higher tag reuse rate. A user study also revealed that different tag sets provided different support for search. Tags produced “with description” shortened the path to the target results, while tags produced without description increased user success in the search task
2015

Kowald Dominik, Lex Elisabeth

Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study

Proceedings of 9th International Conference on Recommender Systems, RecSys'2015, ACM, Vienna, Austria, 2015

Konferenz
To date, the evaluation of tag recommender algorithms has mostly been conducted in limited ways, including p-core pruned datasets, a small set of compared algorithms and solely based on recommender accuracy. In this study, we use an open-source evaluation framework to compare a rich set of state-of-the-art algorithms in six unfiltered, open datasets via various metrics, measuring not only accuracy but also the diversity, novelty and computational costs of the approaches. We therefore provide a transparent and reproducible tag recommender evaluation in real-world folksonomies. Our results suggest that the efficacy of an algorithm highly depends on the given needs and thus, they should be of interest to both researchers and developers in the field of tag-based recommender systems.
2015

Dennerlein Sebastian, Rella Matthias, Tomberg Vladimir, Theiler Dieter, Treasure-Jones Tamsin, Kerr Micky, Ley Tobias, Al-Smadi Mohammad, Trattner Christoph

Making Sense of Bits and Pieces: A Sensemaking Tool for Informal Workplace Learning

European Conference on Technology Enhanced Learning, Springer International Publishing, 2015

Konferenz
Sensemaking at the workplace and in educational contexts has beenextensively studied for decades. Interestingly, making sense out of the own wealthof learning experiences at the workplace has been widely ignored. To tackle thisissue, we have implemented a novel sensemaking interface for healthcare professionalsto support learning at the workplace. The proposed prototype supportsremembering of informal experiences from episodic memory followed by sensemakingin semantic memory. Results from an initial study conducted as part ofan iterative co-design process reveal the prototype is being perceived as usefuland supportive for informal sensemaking by study participants from the healthcaredomain. Furthermore, we find first evidence that re-evaluation of collectedinformation is a potentially necessary process that needs further exploration tofully understand and support sensemaking of informal learning experiences.
2015

Gursch Heimo, Ziak Hermann, Kern Roman

Unified Information Access for Knowledge Workers via a Federated Recommender System

Mensch und Computer 2015 – Workshopband, Anette Weisbecker, Michael Burmester, Albrecht Schmidt, De Gruyter Oldenbourg, Berlin, 2015

Konferenz
The objective of the EEXCESS (Enhancing Europe’s eXchange in Cultural Educational and Scientific reSources) project is to develop a system that can automatically recommend helpful and novel content to knowledge workers. The EEXCESS system can be integrated into existing software user interfaces as plugins which will extract topics and suggest the relevant material automatically. This recommendation process simplifies the information gathering of knowledge workers. Recommendations can also be triggered manually via web frontends. EEXCESS hides the potentially large number of knowledge sources by semi or fully automatically providing content suggestions. Hence, users only have to be able to in use the EEXCESS system and not all sources individually. For each user, relevant sources can be set or auto-selected individually. EEXCESS offers open interfaces, making it easy to connect additional sources and user program plugins.
2015

Veas Eduardo Enrique, di Sciascio Maria Cecilia

Interactive topic analysis with visual analytics and recommender systems.

IJCAI 2015 Workshop on Cognitive Knowledge Acquisition and Applications (Cognitum 2015), 2015

Konferenz
The ability to analyze and organize large collections,to draw relations between pieces of evidence, to buildknowledge, are all part of an information discovery process.This paper describes an approach to interactivetopic analysis, as an information discovery conversationwith a recommender system. We describe a modelthat motivates our approach, and an evaluation comparinginteractive topic analysis with state-of-the-art topicanalysis methods.
2015

Pimas Oliver, Kröll Mark, Kern Roman

Know-Center at PAN 2015 author identification

Lecture Notes in Computer Science, Working Notes Papers of the CLEF 2015 Evaluation Labs, Springer Link, Toulouse, France, 2015

Konferenz
Our system for the PAN 2015 authorship verification challenge is basedupon a two step pre-processing pipeline. In the first step we extract different fea-tures that observe stylometric properties, grammatical characteristics and purestatistical features. In the second step of our pre-processing we merge all thosefeatures into a single meta feature space. We train an SVM classifier on the gener-ated meta features to verify the authorship of an unseen text document. We reportthe results from the final evaluation as well as on the training datasets
2015

Silva Nelson, Eggeling Eva, Schreck Tobias, Fellner Dieter W.

Increasing Fault Tolerance in Operational Centres Using Human Sensing Technologies: Approach and Initial Results

European Project Space on Computer Vision, 2015

Buch
2015

Veas Eduardo Enrique, di Sciascio Maria Cecilia

Interactive Preference Elicitation for Scientific and Cultural Recommendations

IJCAI 2015 Workshop on INTELLIGENT PERSONALIZATION (IP'2015), CEUR-WS, 2015

Konferenz
This paper presents a visual interface developed on the basis of control and transparency to elicit preferences in the scientific and cultural domain. Preference elicitation is a recognized challenge in user modeling for personalized recommender systems. The amount of feedback the user is willing to provide depends on how trustworthy the system seems to be and how invasive the elicitation process is. Our approach ranks a collection of items with a controllable text analytics model. It integrates control with the ranking and uses it as implicit preference for content based recommendations.
2015

Trattner Christoph, Parra Denis , Brusilovsky Peter, , Marinho Leandro

SPS'15: 2015 International Workshop on Social Personalization & Search

Proceedings of the 38th International {ACM} {SIGIR} Conference on Research and Development in Information Retrieval, ACM, 2015

Konferenz
2015

Trattner Christoph, Balby Marinho Leandro, Parra Denis

Are Real-World Place Recommender Algorithms Useful in Virtual World Environments?

Proceedings of the 9th {ACM} Conference on Recommender Systems, ACM, 2015

Konferenz
Large scale virtual worlds such as massive multiplayer online gamesor 3D worlds gained tremendous popularity over the past few years.With the large and ever increasing amount of content available, virtualworld users face the information overload problem. To tacklethis issue, game-designers usually deploy recommendation serviceswith the aim of making the virtual world a more joyful environmentto be connected at. In this context, we present in this paper the resultsof a project that aims at understanding the mobility patternsof virtual world users in order to derive place recommenders forhelping them to explore content more efficiently. Our study focuson the virtual world SecondLife, one of the largest and mostprominent in recent years. Since SecondLife is comparable to realworldLocation-based Social Networks (LBSNs), i.e., users canboth check-in and share visited virtual places, a natural approach isto assume that place recommenders that are known to work well onreal-world LBSNs will also work well on SecondLife. We have putthis assumption to the test and found out that (i) while collaborativefiltering algorithms have compatible performances in both environments,(ii) existing place recommenders based on geographicmetadata are not useful in SecondLife.
2015

di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique

uRank: Visual analytics approach for search result exploration

Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on, IEEE, 2015

Konferenz
uRankis a Web-based tool combining lightweight text analyticsand visual methods for topic-wise exploration of document sets.It includes a view summarizing the content of the document setin meaningful terms, a dynamic document ranking view and a de-tailed view for further inspection of individual documents. Its ma-jor strength lies in how it supports users in reorganizing documentson-the-fly as their information interests change. We present a pre-liminary evaluation showing that uRank helps to reduce cognitiveload compared to a traditional list-based representation.
2014

Stegmaier Florian, Seifert Christin, Kern Roman, Höfler Patrick, Bayerl Sebastian, Granitzer Michael, Kosch Harald, Lindstaedt Stefanie , Mutlu Belgin, Sabol Vedran, Schlegel Kai

Unleashing semantics of research data

Specifying Big Data Benchmarks, Springer, Berlin, Heidelberg, 2014

Buch
Research depends to a large degree on the availability and quality of primary research data, i.e., data generated through experiments and evaluations. While the Web in general and Linked Data in particular provide a platform and the necessary technologies for sharing, managing and utilizing research data, an ecosystem supporting those tasks is still missing. The vision of the CODE project is the establishment of a sophisticated ecosystem for Linked Data. Here, the extraction of knowledge encapsulated in scientific research paper along with its public release as Linked Data serves as the major use case. Further, Visual Analytics approaches empower end users to analyse, integrate and organize data. During these tasks, specific Big Data issues are present.
2014

Mutlu Belgin, Tschinkel Gerwald, Veas Eduardo Enrique, Sabol Vedran, Stegmaier Florian, Granitzer Michael

Suggesting visualisations for published data

Information Visualization Theory and Applications (IVAPP), 2014 International Conference on, IEEE, 2014

Konferenz
Research papers are published in various digital libraries, which deploy their own meta-models and tech-nologies to manage, query, and analyze scientific facts therein. Commonly they only consider the meta-dataprovided with each article, but not the contents. Hence, reaching into the contents of publications is inherentlya tedious task. On top of that, scientific data within publications are hardcoded in a fixed format (e.g. tables).So, even if one manages to get a glimpse of the data published in digital libraries, it is close to impossibleto carry out any analysis on them other than what was intended by the authors. More effective querying andanalysis methods are required to better understand scientific facts. In this paper, we present the web-basedCODE Visualisation Wizard, which provides visual analysis of scientific facts with emphasis on automatingthe visualisation process, and present an experiment of its application. We also present the entire analyticalprocess and the corresponding tool chain, including components for extraction of scientific data from publica-tions, an easy to use user interface for querying RDF knowledge bases, and a tool for semantic annotation ofscientific data set
2014

Rauch Manuela, Wozelka Ralph, Veas Eduardo Enrique, Sabol Vedran

Semantic Blossom Graph: A new Approach for Visual Graph Exploration

18th International Conference on Information Visualisation, IEEE Computer Society CPS, 2014

Konferenz
Graphs are widely used to represent relationshipsbetween entities. Indeed, their simplicity in depicting connect-edness backed by a mathematical formalism, make graphs anideal metaphor to convey relatedness between entities irrespec-tive of the domain. However, graphs pose several challenges forvisual analysis. A large number of entities or a densely con-nected set quickly render the graph unreadable due to clutter.Typed relationships leading to multigraphs cannot clearly berepresented in hierarchical layout or edge bundling, commonclutter reduction techniques. We propose a novel approach tovisual analysis of complex graphs based on two metaphors:semantic blossom and selective expansion. Instead of showingthe whole graph, we display only a small representative subsetof nodes, each with a compressed summary of relations in asemantic blossom. Users apply selective expansion to traversethe graph and discover the subset of interest. A preliminaryevaluation showed that our approach is intuitive and usefulfor graph exploration and provided insightful ideas for futureimprovements.
2014

Silva Nelson

The Sixth Sense of an Air Traffic Controller - Increasing Fault Tolerance of Human Machine Interfaces

SID 2014, 2014

Konferenz
2014

Lex Elisabeth, Kraker Peter, Dennerlein Sebastian

What Really Works: Reflections on Applied Methods in a Real World Interdisciplinary Project

Interdisciplinary Coups to Calamities Workshop at ACM Web Science, 2014

Today’s data driven world requires interdisciplinary, teamoriented approaches: experts from different disciplines are needed to collaboratively solve complex real-world problems. Interdisciplinary teams face a set of challenges that are not necessarily encountered by unidisciplinary teams, such as organisational culture, mental and financial barriers. We share our experiences with interdisciplinary teamwork based on a real-world example. We found that models of interdisciplinary teamwork from Social Sciences and Web Science can guide interdisciplinary teamwork in the domain of pharmaceutical knowledge management. Additionally, we identified potential extensions of the models’ components as well as novel influencing factors such the willingness to explicate and share domain knowledge.
2014

Dennerlein Sebastian, Cook John, Kravcik Milos, Kunzmann Christine, Pata Kai, Purma Jukka, Sandars John, Santos Patricia , Schmidt Andreas, Al-Smadi Mohammad, Trattner Christoph, Ley Tobias

Scaling informal learning at the workplace: A model and four designs from a large‐scale design‐based research effort

British Journal of Educational Technology, 2014

Workplace learning happens in the process and context of work, is multi-episodic, often informal, problem based and takes place on a just-in-time basis. While this is a very effective means of delivery, it also does not scale very well beyond the immediate context. We review three types of technologies that have been suggested to scale learning and three connected theoretical discourses around learning and its support. Based on these three strands and an in-depth contextual inquiry into two workplace learning domains, health care and building and construction, four design-based research projects were conducted that have given rise to designs for scaling informal learning with technology. The insights gained from the design and contextual inquiry contributed to a model that provides an integrative view on three informal learning processes at work and how they can be supported with technology: (1) task performance, reflection and sensemaking; (2) help seeking, guidance and support; and (3) emergence and maturing of collective knowledge. The model fosters our understanding of how informal learning can be scaled and how an orchestrated set of technologies can support this process.
2014

Tschinkel Gerwald, Veas Eduardo Enrique, Mutlu Belgin, Sabol Vedran

Using semantics for interactive visual analysis of linked open data

Proceedings of the 2014 International Conference on Posters & Demonstrations Track-Volume 1272, CEUR-WS. org, 2014

Konferenz
Providing easy to use methods for visual analysis of LinkedData is often hindered by the complexity of semantic technologies. Onthe other hand, semantic information inherent to Linked Data providesopportunities to support the user in interactively analysing the data. Thispaper provides a demonstration of an interactive, Web-based visualisa-tion tool, the “Vis Wizard”, which makes use of semantics to simplify theprocess of setting up visualisations, transforming the data and, most im-portantly, interactively analysing multiple datasets using brushing andlinking method
2014

Ullrich Torsten, Silva Nelson, Eggeling Eva, Fellner Dieter W.

Generative Modeling and Numerical Optimization for Energy Efficient Buildings (3)

Selected Readings in Computer Graphics , 2014

Buch
2014

Silva Nelson, Settgast Volker, Eggeling Eva, Grill Florian, Zeh Theodor, Fellner Dieter W.

Sixth Sense - Air Traffic Control Prediction Scenario Augmented by Sensors

I-Know 2014, 2014

Konferenz
2014

Silva Nelson

The Sixth Sense in Air Traffic Control - Automated error detection through sensor augmentation, while keeping the humans in the main decision loop of ATC

I-Know 2014, 2014

Konferenz
2014

Pammer-Schindler Viktoria, Simon Jörg Peter, Wilding Karin, Keller Stephan, Scherer Reinhold

Designing for Engaging BCI Training: A Jigsaw Puzzle

Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play (CHI Play 2015), 2014

Konferenz
Brain-computer interface (BCI) technology translatesbrain activity to machine-intelligible patterns, thusserving as input “device” to computers. BCI traininggames make the process of acquiring training data forthe machine learning more engaging for the users. Inthis work, we discuss the design space for BCI traininggames based on existing literature, and a traininggame in form of a Jigsaw Puzzle. The game wastrialled with four cerebral palsy patients. All patientswere very acceptant of the involved technology, which,we argue, relates back to the concept of BCI traininggames plus the adaptations we made. On the otherhand, the data quality was unsatisfactory. Hence, infuture work both concept and implementation need tobe finetuned to achieve a balance between useracceptance and data quality.
2014

Sabol Vedran, Tschinkel, Veas Eduardo Enrique, Mutlu Belgin, Granitzer Michael

Discovery and visual analysis of linked data for humans

International Semantic Web Conference, Springer, Cham, 2014

Konferenz
Linked Data has grown to become one of the largest availableknowledge bases. Unfortunately, this wealth of data remains inaccessi-ble to those without in-depth knowledge of semantic technologies. Wedescribe a toolchain enabling users without semantic technology back-ground to explore and visually analyse Linked Data. We demonstrateits applicability in scenarios involving data from the Linked Open DataCloud, and research data extracted from scientific publications. Our fo-cus is on the Web-based front-end consisting of querying and visuali-sation tools. The performed usability evaluations unveil mainly positiveresults confirming that the Query Wizard simplifies searching, refiningand transforming Linked Data and, in particular, that people using theVisualisation Wizard quickly learn to perform interactive analysis taskson the resulting Linked Data sets. In making Linked Data analysis ef-fectively accessible to the general public, our tool has been integratedin a number of live services where people use it to analyse, discover anddiscuss facts with Linked Data.
2014

Granitzer MIchael, Veas Eduardo Enrique, Seifert C.

Linked Data Query Wizard: A Novel Interface for Accessing SPARQL Endpoints.

LDOW, 2014

Konferenz
In an interconnected world, Linked Data is more importantthan ever before. However, it is still quite di cult to accessthis new wealth of semantic data directly without havingin-depth knowledge about SPARQL and related semantictechnologies. Also, most people are currently used to consumingdata as 2-dimensional tables. Linked Data is by de -nition always a graph, and not that many people are used tohandle data in graph structures. Therefore we present theLinked Data Query Wizard, a web-based tool for displaying,accessing, ltering, exploring, and navigating Linked Datastored in SPARQL endpoints. The main innovation of theinterface is that it turns the graph structure of Linked Datainto a tabular interface and provides easy-to-use interactionpossibilities by using metaphors and techniques from currentsearch engines and spreadsheet applications that regular webusers are already familiar with.
2013

Kalkofen Denis, Veas Eduardo Enrique, Zollmann Stefanie, Steinberger Markus, Schmalstieg Dieter

Adaptive ghosted views for augmented reality

Mixed and Augmented Reality (ISMAR), 2013 IEEE International Symposium on, IEEE, 2013

Konferenz
In Augmented Reality (AR), ghosted views allow a viewer to ex-plore hidden structure within the real-world environment. A bodyof previous work has explored which features are suitable to sup-port the structural interplay between occluding and occluded ele-ments. However, the dynamics of AR environments pose seriouschallenges to the presentation of ghosted views. While a modelof the real world may help determine distinctive structural features,changes in appearance or illumination detriment the composition ofoccluding and occluded structure. In this paper, we present an ap-proach that considers the information value of the scene before andafter generating the ghosted view. Hereby, a contrast adjustment ofpreserved occluding features is calculated, which adaptively variestheir visual saliency within the ghosted view visualization. This al-lows us to not only preserve important features, but to also supporttheir prominence after revealing occluded structure, thus achieving a positive effect on the perception of ghosted views.
2013

Ley Tobias, Cook John, Dennerlein Sebastian, Kravcik Milos, Kunzmann Christine, Laanpere Mart, Pata Kai, Purma Jukka, Sandars John, Santos Patricia, Schmidt Andreas

Scaling Informal Learning: An Integrative Systems View on Scaffolding at the Workplace

European Conference on Technology Enhanced Learning, Springer Berlin Heidelberg, 2013

While several technological advances have been suggested to scale learning at the workplace, none has been successful to scale informal learning. We review three theoretical discourses and suggest an integrated systems model of scaffolding informal workplace learning that has been created to tackle this challenge. We derive research questions that emerge from this model and illustrate these with an in-depth analysis of two workplace learning domains.
2013

Dennerlein Sebastian, Gutounig Robert, Kraker Peter, Kaiser René, Rauter Romana , Ausserhofer Julian

Assessing Barcamps: Incentives for Participation in Ad-hoc Conferences and the Role of Social Media

Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies, ACM, 2013

Barcamps are informal conferences whose content is not de-fined in advance, often referred to as ad-hoc conferences orun-conferences. Therefore, the outcomes of a barcamp arelargely unknown before the event. This raises the question ofthe participants’ motivations to attend and contribute. Toanswer this question, we conducted an exploratory empiricalstudy at Barcamp Graz 2012. We applied a mixed-methodapproach: first we used a socio-demographic questionnaire(n=99) which allowed us to characterize the ’typical barcamper’.Second, we conducted qualitative interviews (n=10) toget a deeper understanding of the participants’ motivationsto attend, expectations, and the use of social media in thatcontext. We identified three concepts, which could be deductedfrom the interviews: people, format and topics. Wefound that the motivation to attend and even a commonidentity is quite strongly based on these three factors. Furthermore,the results indicate that participants share a set ofactivities and methods by following the barcamp’s inherentrules and make extensive use of social media.
2013

Cook John, Santos Patricia, Ley Tobias, Dennerlein Sebastian, Pata Kai, Colley Joanna, Sandars John, Treasure-Jones Tamsin

D2. 1 Concept & Prototype Networked Scaffolding Layer

2013

2013

Dennerlein Sebastian, Santos Patricia, Kämäräinen Pekka , Deitmer Ludger , Heinemann Lars , Campbell Melanie, Dertl Michael, Bachl Martin, Trattner Christoph, Bauters Merja

Sharing Turbine: Bridging Informal and Formal Learning for their Mutual Enrichment

DOI, 2013

Being able to connect informal and formal learning experiences is thekey to successful apprenticeships. For instance the knowledge emerging out ofpractice should be used to extend and refine formal leaning experiences, andvice versa. Currently such scenarios are not supported appropriately withtechnology in many different domains. This paper focuses on the constructiondomain, which is one of the test-beds in the recently started large-scale EUproject ‘Learning Layers’. We suggest a model for bridging this gap betweenformal and informal learning by co-designing with construction sectorrepresentatives to identify how web services, apps and mobile devices can beorchestrated to connect informal and formal learning with the goal of enhancingcollaboration and supporting contextual learning at the workplace.
2013

Dennerlein Sebastian

Understanding and Supporting Intersubjective Meaning Making in Socio-Technical Systems: A Cognitive Psychology Perspective

Doctoral Consortium at the European Conference on Technology Enhanced Learning (EC-TEL 2013), 2013

This dissertation will elaborate on the understanding of intersubjective meaning making by analyzing the traces of collaborative knowledge construction users leave behind in socio-technical systems. Therefore, it will draw upon more theoretical and more formal models of cognitive psychology to describe and explain the underlying process in detail. This is done with the goal to support intersubjective meaning making and thus elevate informal collaborative knowledge construction in nowadays affordances of social media.
2013

Tatzgern Markus, Grasset Raphael, Veas Eduardo Enrique, Kalkofen Denis, Schmalstieg Dieter

Exploring Distant Objects with Augmented Reality.

EGVE/EuroVR, 2013

Konferenz
Augmented reality (AR) enables users to retrieve additional information about the real world objects and locations.Exploring such location-based information in AR requires physical movement to different viewpoints, which maybe tiring and even infeasible when viewpoints are out of reach. In this paper, we present object-centric explorationtechniques for handheld AR that allow users to access information freely using a virtual copy metaphor to explorelarge real world objects. We evaluated our interfaces in controlled conditions and collected first experiences in areal world pilot study. Based on our findings, we put forward design recommendations that should be consideredby future generations of location-based AR browsers, 3D tourist guides, or in situated urban plannin
2013

Kraker Peter, Dennerlein Sebastian

Towards a Model of Interdisciplinary Teamwork for WebScience: What can Social Theory Contribute?

Web Science 2013 Workshop: Harnessing the Power of Social Theory for Web Science, Paris, 2013

In this position paper, we argue that the different disciplinesin Web Science do not work together in an interdisciplinaryway. We attribute this to a fundamental difference in approachingresearch between social scientists and computerscientists, which we call the patterns vs. model problem.We reason that interdisciplinary teamwork is needed toovercome the patterns vs. model problem. We then discusstwo theoretical strains in social science which we see asrelevant in the context of interdisciplinary teamwork. Finally,we sketch a model of interdisciplinary teamwork in WebScience based on the interplay of collaboration and cooperation.
2013

Dennerlein Sebastian, Moskaliuk Johannes , Ley Tobias, Kump Barbara

Tracing knowledge co-evolution in a realistic course setting: A wiki-based field experiment

Computers & Education, Pergamon, 2013

The co-evolution model of collaborative knowledge building by Cress & Kimmerle (2008)assumes that cognitive and social processes interact when users build knowledge with shareddigital artifacts. While these assumptions have been tested in various lab experiments, a testunder natural field conditions in educational settings has not been conducted. Here, wepresent a field experiment where we triggered knowledge co-evolution in an accommodationand an assimilation condition, and measured effects on student knowledge building outsidethe laboratory in the context of two university courses. Therefore, 48 students receiveddifferent kinds of prompts that triggered external accommodation and assimilation whilewriting a wiki text. Knowledge building was measured with a content analysis of the students‟texts and comments (externalization), and with concept maps and association tests(internalization). The findings reveal that (a) different modes of externalization(accommodation and assimilation) could be triggered with prompts, (b) across bothconditions, this externalization co-occurred with internalization (student learning), and (c)there is some evidence that external assimilation and accommodation had differential effectson internal assimilation and accommodation. Thus, the field experiment supports theassumptions of the co-evolution model in a realistic course setting. On a more general note,the study provides an example of how wikis can be used successfully for collaborativeknowledge building within educational contexts.
2013

Höfler Patrick, Granitzer Michael, Sabol Vedran, Lindstaedt Stefanie

Linked Data Query Wizard: A Tabular Interface for the Semantic Web

ESWC 2013, LNCS 7882, P. Cimiano et al., Springer, Heidelberg, 2013

Konferenz
Linked Data has become an essential part of the Semantic Web. A lot of Linked Data is already available in the Linked Open Data cloud, which keeps growing due to an influx of new data from research and open government activities. However, it is still quite difficult to access this wealth of semantically enriched data directly without having in-depth knowledge about SPARQL and related semantic technologies. In this paper, we present the Linked Data Query Wizard, a prototype that provides a Linked Data interface for non-expert users, focusing on keyword search as an entry point and a tabular interface providing simple functionality for filtering and exploration.
2013

Silva Nelson

The 6th Sense of an Air Traffic Controller Increasing Fault Tolerance of Human Machine Interfaces

SID 2013, 2013

Konferenz
2013

Breitweiser Christian, Terbu Oliver, Holzinger Andreas, Brunner Clemens, Lindstaedt Stefanie , Müller-Putz Gernot

iScope - Viewing Biosignals on Mobile Devices.

Joint International Conference on Pervasive Computing and the Networked World, Qiaohong Zu, Bo Hu, Atilla Elçi , Springer, 2013

Journal
We developed an iOS based application called iScope to monitor biosignals online. iScope is able to receive different signal types via a wireless network connection and is able to present them in the time or the frequency domain. Thus it is possible to inspect recorded data immediately during the recording process and detect potential artifacts early without the need to carry around heavy equipment like laptops or complete PC workstations. The iScope app has been tested during various measurements on the iPhone 3GS as well as on the iPad 1 and is fully functional.
2013

Ullrich Torsten, Silva Nelson, Eggeling Eva, Fellner Dieter W.

Generative Modeling and Numerical Optimization for Energy Efficient Buildings (2)

Proceedings of 2013 / OCG Energy Informatics , 2013

Buch
2013

Kraker Peter, Trattner Christoph, Jack Kris, Lindstaedt Stefanie , Schlgl Christian

Head Start: Improving Academic Literature Search with Overview Visualizations based on Readership Statistics

Web Science 2013, 2013

Konferenz
At the beginning of a scientific study, it is usually quite hardto get an overview of a research field. We aim to addressthis problem of classic literature search using web data. Inthis extended abstract, we present work-in-progress on aninteractive visualization of research fields based on readershipstatistics from the social reference management systemMendeley. To that end, we use library co-occurrences as ameasure of subject similarity. In a first evaluation, we findthat the visualization covers current research areas withineducational technology but presents a view that is biasedby the characteristics of readers. With our presentation, wehope to elicit feedback from the Websci’13 audience on (1)the usefulness of the prototype, and (2) how to overcomethe aforementioned biases using collaborative constructiontechniques.
2013

Ullrich Torsten, Silva Nelson, Eggeling Eva, Fellner Dieter W.

Generative Modeling and Numerical Optimization for Energy Efficient Buildings (1)

IECON 2013 , 2013

Buch
2012

Drachsler Hendrik, Verbert Katrien, Manouselis Nikos, Vuorikari Riina, Wolpers Martin, Lindstaedt Stefanie

Preface [Special issue on dataTEL–Data Supported Research in Technology-Enhanced Learning]

Int. J. Technology Enhanced Learning, 2012

Journal
Technology Enhanced Learning is undergoing a significant shift in paradigm towards more data driven systems that will make educational systems more transparent and predictable. Data science and data-driven tools will change the evaluation of educational practice and didactical interventions for individual learners and educational institutions. We summarise these developments and new challenges in the preface of this Special Issue under the keyword dataTEL that stands for ‘Data-Supported Technology-Enhanced Learning’.
2012

Pammer-Schindler Viktoria, Kump Barbara, Lindstaedt Stefanie

Tag-based algorithms can predict human ratings of which objects a picture shows

Multimedia Tools and Applications, Springer US, 2012

Journal
Collaborative tagging platforms allow users to describe resources with freely chosen keywords, so called tags. The meaning of a tag as well as the precise relation between a tag and the tagged resource are left open for interpretation to the user. Although human users mostly have a fair chance at interpreting this relation, machines do not. In this paper we study the characteristics of the problem to identify descriptive tags, i.e. tags that relate to visible objects in a picture. We investigate the feasibility of using a tag-based algorithm, i.e. an algorithm that ignores actual picture content, to tackle the problem. Given the theoretical feasibility of a well-performing tag-based algorithm, which we show via an optimal algorithm, we describe the implementation and evaluation of a WordNet-based algorithm as proof-of-concept. These two investigations lead to the conclusion that even relatively simple and fast tag-based algorithms can yet predict human ratings of which objects a picture shows. Finally, we discuss the inherent difficulty both humans and machines have when deciding whether a tag is descriptive or not. Based on a qualitative analysis, we distinguish between definitional disagreement, difference in knowledge, disambiguation and difference in perception as reasons for disagreement between raters.
2012

Seitlinger Christian, Schöfegger Karin, Lindstaedt Stefanie , Ley Tobias

Community-orientiertes Lernen

CSCL-Kompendium-Lehr-und Handbuch zum computerunterstützten kooperativen Lernen, Oldenburg, 2012

Buch
2012

Kump Barbara, Seifer Christin, Beham Günter, Lindstaedt Stefanie , Ley Tobias

Seeing What the System Thinks You Know - Visualizing Evidence in an Open Learner Model

Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, ACM, Canada, 2012

User knowledge levels in adaptive learning systems can be assessed based on user interactions that are interpreted as Knowledge Indicating Events (KIE). Such an approach makes complex inferences that may be hard to understand for users, and that are not necessarily accurate. We present MyExperiences, an open learner model designed for showing the users the inferences about them, as well as the underlying data. MyExperiences is one of the first open learner models based on tree maps. It constitutes an example of how research into open learner models and information visualization can be combined in an innovative way.
2011

Lindstaedt Stefanie , Kraker Peter, Wild Fridolin, Ullmann Thomas, Duval Erik, Parra Gonzalo

First experiences with a Science 2.0 infrastructure

2011

This deliverable reports on first usage experiences and evaluations of the STELLAR Science 2.0 Infrastructure. Usage experiences were available predominantly for the "mature" part of the infrastructure provided by standard Web 2.0 tools adapted to STELLAR needs. Evaluations are provided for newly developed tools. We first provide an overview of the whole STELLAR Science 2.0 Infrastructure and the relationships between the building blocks. While the individual building blocks already benefit researchers, the integration between them is the key for a positive usage experience. The publication meta data ecosystem for example provides researchers with an easy to retrieve set of TEL related data. Tools like the ScienceTable, Muse, the STELLAR latest publication widget, and the STELLAR BuRST search show already several scenarios of how to make use of this infrastructure. Especially a strong focus on anlytical tools based on publication and social media data seem useful. In order to highlight the relevance of the infrastructure to the individual capacitiy building activties within STELLAR, the usage experiences of individual building blocks are then reported with respect to Researcher Capacity (e.g. Deliverable Wikis, More! application), Doctoral Academy Capacity (e.g. DoCoP), Community Level Capacity (e.g TELeurope), and Leadership Capacity (e.g. Meeting of Minds, Podcast Series). Here we draw from 11 scientific papers published. The reader will find an overview of all these papers in the Appendix. Based on the usage experiences and evaluations we have identified a number of ideas which might be worth considering for future developments. For example, the experiences gained with the Deliverable Wikis show how the modification of the standard Wiki history can provide useful analytical insights into the collaboration of living deliverables and can return the focus on authorship (which is intentionally masked in Wikis, because of their strong notion on the product and not on authors). We conclude with main findings and an outlook on the development plan and evaluation plan which are currently being developed and which will influence D6.6. Particularly, we close with the notion of a Personal Research Environment (PRE) which draws from the concept of Personal Learning Environments (PLE).
2011

Automatic detection of accommodation steps as an indicator of knowledge maturing (in-press)

Interacting with computers: the interdisciplinary journal of human-computer interaction, Murray, D. M., Elsevier B.V., 2011

Konferenz
Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed.
2011

Granitzer Michael, Lindstaedt Stefanie

Knowledge Work : Knowledge Worker Productivity , Collaboration and User Support

J.UCS - Journal of Universal Computer Science, 2011

Journal
2011

Shahzad Syed K, Granitzer Michael, Helic Denis

Ontological Model Driven GUI Development: User Interface Ontology Approach

6th International Conference on Computer Sciences and Convergence Information Technology (ICCIT), IEEE, 2011

Konferenz
Ontology and Semantic Framework has becomepervasive in computer science. It has huge impact at database,business logic and user interface for a range of computerapplications. This framework is also being introduced, presentedor plugged at user interfaces for various software and websites.However, establishment of structured and standardizedontological model based user interface development environmentis still a challenge. This paper talks about the necessity of such anenvironment based on User Interface Ontology (UIO). To explorethis phenomenon, this research focuses at the User Interfaceentities, their semantics, uses and relationships among them. Thefirst part focuses on the development of User Interface Ontology.In the second step, this ontology is mapped to the domainontology to construct a User Interface Model. Finally, theresulting model is quantified and instantiated for a user interfacedevelopment to support our framework. This UIO is anextendable framework that allows defining new sub-conceptswith their ontological relationships and constraints.
2011

Kraker Peter, Wagner Claudia, Jeanquartier Fleur, Lindstaedt Stefanie

On the Way to a Science Intelligence: Visualizing TEL Tweets for Trend Detection

Sixth European Conference on Technology Enhanced Learning (EC-TEL 2011), Springer, 2011

Konferenz
This paper presents an adaptable system for detecting trends based on the micro-blogging service Twitter, and sets out to explore to what extent such a tool can support researchers. Twitter has high uptake in the scientific community, but there is a need for a means of extracting the most important topics from a Twitter stream. There are too many tweets to read them all, and there is no organized way of keeping up with the backlog. Following the cues of visual analytics, we use visualizations to show both the temporal evolution of topics, and the relations between different topics. The Twitter Trend Detection was evaluated in the domain of Technology Enhanced Learning (TEL). The evaluation results indicate that our prototype supports trend detection but reveals the need for refined preprocessing, and further zooming and filtering facilities.
2011

Horn Christopher, Pimas Oliver, Granitzer Michael, Lex Elisabeth

Realtime Ad Hoc Search in Twitter: Know-Center at TREC Microblog Track 2011

Proceedings of TREC 2011, 2011

In this paper, we outline our experiments carried out at theTREC Microblog Track 2011. Our system is based on a plain text indexextracted from Tweets crawled from twitter.com. This index hasbeen used to retrieve candidate Tweets for the given topics. The resultingTweets were post-processed and then analyzed using three differentapproaches: (i) a burst detection approach, (ii) a hashtag analysis, and(iii) a Retweet analysis. Our experiments consisted of four runs: Firstly,a combination of the Lucene ranking with the burst detection, and secondly,a combination of the Lucene ranking, the burst detection, and thehashtag analysis. Thirdly, a combination of the Lucene ranking, the burstdetection, the hashtag analysis, and the Retweet analysis, and fourthly,again a combination of the Lucene ranking with the burst detection butin this case with more sophisticated query language and post-processing.We achieved the best MAP values overall in the fourth run.
2011

Lindstaedt Stefanie , Kump Barbara, Rath Andreas S.

Context-Aware Recommendation for Work-Integrated Learning

Context and Semantics for Knowledge Management. Technologies for Personal Productivity, Warren, P., Davies, J., Simperl, E., Springer, 2011

Journal
Within this chapter we first outline the important role learning plays within knowledge work and its impact on productivity. As a theoretical background we introduce the paradigm of Work-Integrated Learning (WIL) which conceptualizes informal learning at the workplace and takes place tightly intertwined with the execution of work tasks. Based on a variety of in-depth knowledge work studies we identify key requirements for the design of work-integrated learning support. Our focus is on providing learning support during the execution of work tasks (instead of beforehand), within the work environment of the user (instead of within a separate learning system), and by repurposing content for learning which was not originally intended for learning (instead of relying on the expensive manual creation of learning material). In order to satisfy these requirements we developed a number of context-aware knowledge services. These services integrate semantic technologies with statistical approaches which perform well in the face of uncertainty. These hybrid knowledge services include the automatic detection of a user’s work task, the ‘inference’ of the user’s competencies based on her past activities, context-aware recommendation of content and colleagues, learning opportunities, etc. A summary of a 3 month in-depth summative workplace evaluation at three testbed sites concludes the chapter.
2011

Erdmann Michael, Hansch Daniel, Pammer-Schindler Viktoria, Rospocher Marco, Ghidini Chiara, Lindstaedt Stefanie , Serafini Luciano

Applications of Semantic Wikis - Bringing Complementary Models and People Together: A Semantic Wiki for Enterprise Process and Application Modelling

Context and Semantics for Knowledge Management}, Warren, P., Davies, J., Simperl, E., Springer, 2011

Journal
This chapter describes some extensions to and applications of the Semantic MediaWiki. It complements the discussion of the SMW in Chap. 3. Semantic enterprise wikis combine the strengths of traditional content management systems, databases, semantic knowledge management systems and collaborative Web 2.0 platforms. Section 12.1 presents SMW+, a product for developing semantic enterprise applications. The section describes a number of real-world applications that are realized with SMW+. These include content management, project management and semantic data integration. Section 12.2 presents MoKi, a semantic wiki for modeling enterprise processes and application domains. Example applications of MoKi include modeling tasks and topics for work-integrated learning, collaboratively building an ontology and modeling clinical protocols. The chapter illustrates the wealth of activities which semantic wikis support.
2011

Kump Barbara, Knipfer Kristin, Pammer-Schindler Viktoria, Schmidt Andreas, Maier Ronald, Kunzmann Christine, Cress Ulrike, Lindstaedt Stefanie

The role of Reflection in Maturing Organizational Know-how

Proceedings of the 1st European Workshop on Awareness and Reflection in Learning Networks, Palermo, Italy, September 21, 2011, Reinhardt, W., Ullmann, T. D., Scott, P., Pammer, V., Conlan, O., Berlanga, A., 2011

Konferenz
The Knowledge Maturing Phase Model has been presented as a model aligning knowledge management and organizational learning. The core argument underlying the present paper is that maturing organizational knowhow requires individual and collaborative reflection at work. We present an explorative interview study that analyzes reflection at the workplace in four organizations in different European countries. Our qualitative findings suggest that reflection is not equally self-evident in different settings. A deeper analysis of the findings leads to the hypothesis that different levels of maturity of processes come along with different expectations towards the workers with regard to compliance and flexibility, and to different ways of how learning at work takes place. Furthermore, reflection in situations where the processes are in early maturing phases seems to lead to consolidation of best practice, while reflection in situations where processes are highly standardized may lead to a modification of these standard processes. Therefore, in order to support the maturing of organizational know-how by providing reflection support, one should take into account the degree of standardisation of the processes in the target group.
2011

Seifert Christin, Ulbrich Eva Pauline, Granitzer Michael

Word Clouds for Efficient Document Labeling

The Fourteenth International Conference on Discovery Science (DS 2011), Lecture Notes in Computer Science, Springer, 2011

Konferenz
In text classification the amount and quality of training datais crucial for the performance of the classifier. The generation of trainingdata is done by human labelers - a tedious and time-consuming work. Wepropose to use condensed representations of text documents instead ofthe full-text document to reduce the labeling time for single documents.These condensed representations are key sentences and key phrases andcan be generated in a fully unsupervised way. The key phrases are presentedin a layout similar to a tag cloud. In a user study with 37 participantswe evaluated whether document labeling with these condensedrepresentations can be done faster and equally accurate by the humanlabelers. Our evaluation shows that the users labeled word clouds twiceas fast but as accurately as full-text documents. While further investigationsfor different classification tasks are necessary, this insight couldpotentially reduce costs for the labeling process of text documents.
2011

Kern Roman, Zechner Mario, Granitzer Michael

Model Selection Strategies for Author Disambiguation

IEEE Computer Society: 8th International Workshop on Text-based Information Retrieval in Procceedings of 22th International Conference on Database and Expert Systems Applications (DEXA 11), IEEE , 2011

Konferenz
Author disambiguation is a prerequisite for utilizingbibliographic metadata in citation analysis. Automaticdisambiguation algorithms mostly rely on cluster-based disambiguationstrategies for identifying unique authors given theirnames and publications. However, most approaches rely onknowing the correct number of unique authors a-priori, whichis rarely the case in real world settings. In this publicationwe analyse cluster-based disambiguation strategies and developa model selection method to estimate the number of distinctauthors based on co-authorship networks. We show that, givenclean textual features, the developed model selection methodprovides accurate guesses of the number of unique authors.
2010

Lex Elisabeth, Granitzer Michael, Juffinger A.

Facet Classification of Blogs: Know-Center at the TREC 2009 Blog Distillation Task

Proceedings of the 18th Text REtrieval Conference, 2010

Konferenz
In this paper, we outline our experiments carried out at the TREC 2009 Blog Distillation Task. Our system is based on a plain text index extracted from the XML feeds of the TREC Blogs08 dataset. This index was used to retrieve candidate blogs for the given topics. The resulting blogs were classified using a Support Vector Machine that was trained on a manually labelled subset of the TREC Blogs08 dataset. Our experiments included three runs on different features: firstly on nouns, secondly on stylometric properties, and thirdly on punctuation statistics. The facet identification based on our approach was successful, although a significant number of candidate blogs were not retrieved at all.
2010

Ley Tobias, Kump Barbara, Gerdenitsch C.

Scaffolding Self-directed Learning with Personalized Learning Goal Recommendations

Conference on User Modeling, Adaptation and Personalization - UMAP 2010, Springer, 2010

Konferenz
Adaptive scaffolding has been proposed as an efficient means for supporting self-directed learning both in educational as well as in adaptive learning systems research. However, the effects of adaptation on self-directed learning and the differential contributions of different adaptation models have not been systematically examined. In this paper, we examine whether personalized scaffolding in the learning process improves learning. We conducted a controlled lab study in which 29 students had to solve several tasks and learn with the help of an adaptive learning system in a within-subjects control condition design. In the learning process, participants obtained recommendations for learning goals from the system in three conditions: fixed scaffolding where learning goals were generated from the domain model, personalized scaffolding where these recommendations were ranked according to the user model, and random suggestions of learning goals (control condition). Students in the two experimental conditions clearly outperformed students in the control condition and felt better supported by the system. Additionally, students who received personalized scaffolding selected fewer learning goals than participants from the other groups.
2010

Wolpers Martin, Kirschner Paul A., Scheffel Maren, Lindstaedt Stefanie , Dimitrova Vania

Sustaining TEL: From Innovation to Learning and Practice

Springer Verlag, Wolpers, M., Kirschner, P. A., Scheffel, M., Lindstaedt, S. N., Dimitrova, V. , Springer, 2010

Buch
2010

Schachner W.

Wissen wirkt in Prozessen

Wissensmanagement in der Praxis – Fokus Prozessmanagement, Shaker Verlag, 2010

Buch
2010

Granitzer Michael, Kienreich Wolfgang

Semantische Technologien: Stand der Forschung und Visionen

Internationales Rechtsinformatik Symposion (IRIS 10), OCG, 2010

Konferenz
2010

Granitzer Michael

Adaptive Term Weighting through Stochastic Optimization

11th International Conference, CICLing 2010, Iasi, Romania, March 22-25, 2010, Gelbukh, A., Springer, 2010

Konferenz
Term weighting strongly influences the performance of text miningand information retrieval approaches. Usually term weights are determined throughstatistical estimates based on static weighting schemes. Such static approacheslack the capability to generalize to different domains and different data sets. Inthis paper, we introduce an on-line learning method for adapting term weightsin a supervised manner. Via stochastic optimization we determine a linear transformationof the term space to approximate expected similarity values amongdocuments. We evaluate our approach on 18 standard text data sets and showthat the performance improvement of a k-NN classifier ranges between 1% and12% by using adaptive term weighting as preprocessing step. Further, we provideempirical evidence that our approach is efficient to cope with larger problems
2010

Schachner W.

Wissen schafft Projektperformance

Wissensmanagement in der Praxis - Fokus Projektmanagement, Shaker Verlag, 2010

Buch
2010

Granitzer Michael, Kienreich Wolfgang, Sabol Vedran, Lex Elisabeth

Knowledge Relationship Discovery and Visually Enhanced Access for the Media Domain

Medien-Wissen-Bildung. Explorationen visualisierter und kollaborativer Wissensräume, Innsbruck University Press, 2010

Konferenz
Technological advances and paradigmatic changes in the utilization of the World Wide Web havetransformed the information seeking strategies of media consumers and invalidated traditionalbusiness models of media providers. We discuss relevant aspects of this development and presenta knowledge relationship discovery pipeline to address the requirements of media providers andmedia consumers. We also propose visually enhanced access methods to bridge the gap betweencomplex media services and the information needs of the general public. We conclude that acombination of advanced processing methods and visualizations will enable media providers totake the step from content-centered to service-centered business models and, at the same time,will help media consumers to better satisfy their personal information needs.
2010

Stocker A., Mueller J.

Enterprise Microblogging bei Siemens, Building Technologies Division

erscheint in: DOK Magazin, 2010

Journal
2010

Ley Tobias, Kump Barbara, Albert D.

A Methodology for Eliciting, Modelling, and Evaluating Expert Knowledge for an Adaptive Work-integrated Learning System

International Journal of Human-Computer Studies, 2010

Journal
2010

Lex Elisabeth, Granitzer Michael, Juffinger A.

A Comparison of Stylometric and Lexical Features for Web Genre Classification and Emotion Classification in Blogs

IEEE Computer Society: 7th International Workshop on Text-based Information Retrieval in Procceedings of 21th International Conference on Database and Expert Systems Applications (DEXA 10)., IEEE, 2010

Konferenz
In the blogosphere, the amount of digital content is expanding and for search engines, new challenges have been imposed. Due to the changing information need, automatic methods are needed to support blog search users to filter information by different facets. In our work, we aim to support blog search with genre and facet information. Since we focus on the news genre, our approach is to classify blogs into news versus rest. Also, we assess the emotionality facet in news related blogs to enable users to identify people’s feelings towards specific events. Our approach is to evaluate the performance of text classifiers with lexical and stylometric features to determine the best performing combination for our tasks. Our experiments on a subset of the TREC Blogs08 dataset reveal that classifiers trained on lexical features perform consistently better than classifiers trained on the best stylometric features.
2010

Kern Roman, Granitzer Michael, Muhr M.

KCDC: Word Sense Induction by Using Grammatical Dependencies and Sentence Phrase Structure

Proceedings of SemEval-2, 2010

Konferenz
Word sense induction and discrimination(WSID) identifies the senses of an ambiguousword and assigns instances of thisword to one of these senses. We have builda WSID system that exploits syntactic andsemantic features based on the results ofa natural language parser component. Toachieve high robustness and good generalizationcapabilities, we designed our systemto work on a restricted, but grammaticallyrich set of features. Based on theresults of the evaluations our system providesa promising performance and robustness.
2010

Lindstaedt Stefanie , Kump Barbara, Beham Günter, Pammer-Schindler Viktoria, Ley Tobias, de Hoog R., Dotan A.

Providing Varying Degrees of Guidance for Work-Integrated Learning

Sustaining TEL: From Innovation to Learning and Practice, Proceedings of EC-TEL 2010, Wolpers, M., Kirschner, P. A., Scheffel, M., Lindstaedt, S. N., Dimitrova, V., Springer, 2010

Konferenz
We present a work-integrated learning (WIL) concept which aims atempowering employees to learn while performing their work tasks. Withinthree usage scenarios we introduce the APOSDLE environment whichembodies the WIL concept and helps knowledge workers move fluidly alongthe whole spectrum of WIL activities. By doing so, they are experiencingvarying degrees of learning guidance: from building awareness, over exposingknowledge structures and contextualizing cooperation, to triggering reflectionand systematic competence development. Four key APOSDLE components areresponsible for providing this variety of learning guidance. The challenge intheir design lies in offering learning guidance without being domain-specificand without relying on manually created learning content. Our three monthsummative workplace evaluation within three application organizationssuggests that learners prefer awarenss building functionalities and descriptivelearning guidance and reveals that they benefited from it.
2010

Schachner W.

Wissen steigert Unternehmensqualität

Wissensmanagement in der Praxis - Fokus Qualitätsmanagement, Shaker Verlag, 2010

Buch
2010

Kern Roman, Granitzer Michael, Muhr M.

Analysis of Structural Relationships for Hierarchical Cluster Labeling

Proceeding of the 33rd international ACM SIGIR Conference on Research and Development in information Retrieval, ACM, 2010

Konferenz
Cluster label quality is crucial for browsing topic hierarchiesobtained via document clustering. Intuitively, the hierarchicalstructure should influence the labeling accuracy. However,most labeling algorithms ignore such structural propertiesand therefore, the impact of hierarchical structureson the labeling accuracy is yet unclear. In our work weintegrate hierarchical information, i.e. sibling and parentchildrelations, in the cluster labeling process. We adaptstandard labeling approaches, namely Maximum Term Frequency,Jensen-Shannon Divergence, χ2 Test, and InformationGain, to take use of those relationships and evaluatetheir impact on 4 different datasets, namely the Open DirectoryProject, Wikipedia, TREC Ohsumed and the CLEFIP European Patent dataset. We show, that hierarchicalrelationships can be exploited to increase labeling accuracyespecially on high-level nodes.
2010

Scheir Peter, Prettenhofer Peter, Lindstaedt Stefanie , Ghidini Chiara

An associative and adaptive network model for information retrieval in the Semantic Web

Progressive Concepts for Semantic Web Evolution: Applications and Developments, IGI Global, 2010

Buch
While it is agreed that semantic enrichment of resources would lead to better search results, at present the low coverage of resources on the web with semantic information presents a major hurdle in realizing the vision of search on the Semantic Web. To address this problem, this chapter investigates how to improve retrieval performance in settings where resources are sparsely annotated with semantic information. Techniques from soft computing are employed to find relevant material that was not originally annotated with the concepts used in a query. The authors present an associative retrieval model for the Semantic Web and evaluate if and to which extent the use of associative retrieval techniques increases retrieval performance. In addition, the authors present recent work on adapting the network structure based on relevance feedback by the user to further improve retrieval effectiveness. The evaluation of new retrieval paradigms - such as retrieval based on technology for the Semantic Web - presents an additional challenge since no off-the-shelf test corpora exist. Hence, this chapter gives a detailed description of the approach taken to evaluate the information retrieval service the authors have built.
2010

Balacheff, Nicolas, Bottino, Rosa, Fischer, Frank, Hofmann, Lena, Joubert, Marie, Kieslinger, Barbara, Lindstaedt Stefanie , Manca, Stefanie, Ney, Muriel, Pozzi, Francesca, Sutherland, Rosamund, Verbert, Katrien, Timmis, Sue, Wild, Fridolin, Scott, Peter, Specht, Marcus

D1.1 The STELLAR vision and strategy statement

2010

This First TEL Grand Challenge Vision and Strategy Report aims to: • provide a unifying framework for members of STELLAR (including doctoral candidates) to develop their own research agenda • engage the STELLAR community in scientific debate and discussion with the long term aim of developing awareness of and respect for different theoretical and methodological perspectives • build knowledge related to the STELLAR grand challenges through the construction of a wiki that is iteratively co‐edited throughout the life of the STELLAR network • develop understandings of the way in which web 2.0 technologies can be used to construct knowledge within a research community (science 2.0) • develop strategies for ways in which the STELLAR instruments can feed into the ongoing development of the wiki and how the they can be used to address the challenges highlighted in this report.
2010

Lindstaedt Stefanie , Beham Günter, Stern Hermann, Drachsler H., Bogers T., Vuorikari R., Verbert K., Duval E., Manouselis N., Friedrich M., Wolpers M.

dataTEL - Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning

1st Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2010), Procedia Computer Science, Elsevier, 2010

Konferenz
This paper raises the issue of missing data sets for recommender systems in Technology Enhanced Learning that can be used asbenchmarks to compare different recommendation approaches. It discusses how suitable data sets could be created according tosome initial suggestions, and investigates a number of steps that may be followed in order to develop reference data sets that willbe adopted and reused within a scientific community. In addition, policies are discussed that are needed to enhance sharing ofdata sets by taking into account legal protection rights. Finally, an initial elaboration of a representation and exchange format forsharable TEL data sets is carried out. The paper concludes with future research needs.
2010

Granitzer Michael, Sabol Vedran, Onn K., Lukose D.

Ontology Alignment - A Survey with Focus on Visually Supported Semi-Automatic Techniques

Future Internet, MDPI AG, 2010

Journal
2010

Pozzi, Francesca, Persico, Donatella, Fischer, Frank, Hofmann, Lena, Lindstaedt Stefanie , Cress, Ulrike, Rath Andreas S., Moskaliuk, Johannes, Weber, Nicolas, Kimmerle, Joachim, Devaurs Didier, Ney, Muriel, Gonçalves, Celso, Balacheff, Nicolas, Schwartz, Claudine, Bosson, Jean-Luc, Dillenbourg, Pierre, Jermann, Patrick, Zufferey, Guillaume, Brown, Elisabeth, Sharples, Mike, Windrum, Caroline, Specht, Marcus, Börner, Dirk, Glahn, Christian, Fiedler, Sebastian, Fisichella, Marco, Herder, Eelco, Marenzi, Ivana, Nejdl, Wolfgang, Kawese, Ricardo, Papadakis, George

D1.2 Trends in Connecting Learners. First Research & Technology Scouting Report

2010

In this first STELLAR trend report we survey the more distant future of TEL, as reflected in the roadmaps; we compare the visions with trends in TEL research and TEL practice. This generic overview is complemented by a number of small-scale studies, which focus on a specific technology, approach or pedagogical model.
2010

Lindstaedt Stefanie , Duval E., Ullmann T.D., Wild F., Scott P.

Proceedings of the 2nd International Workshop on Research 2.0

CEUR Workshop Proceedings, CEUR-WS, 2010

Buch
Research2.0 is in essence a Web2.0 approach to how we do research. Research2.0 creates conversations between researchers, enables them to discuss their findings and connects them with others. Thus, Research2.0 can accelerate the diffusion of knowledge.ChallengesAs concluded during the workshop, at least four challenges are vital for future research.The first area is concerned with availability of data. Access to sanitized data and conventions on how to describe publication-related metadata provided from divergent sources are enablers for researchers to develop new views on their publications and their research area. Additional, social media data gain more and more attention. Reaching a widespread agreement about this for the field of technology-enhanced learning would be already a major step, but it is also important to focus on the next steps: what are success-critical added values driving uptake in the research community as a whole?The second area of challenges is seen in Research 2.0 practices. As technology-enhanced learning is a multidisciplinary field, practices developed in one area could be valuable for others. To extract the essence of successful multidisciplinary Research 2.0 practice though, multidimensional and longitudinal empirical work is needed. It is also an open question, if we should support practice by fostering the usage of existing tools or the development of new tools, which follow Research 2.0 principles. What makes a practice sustainable? What are the driving factors?The third challenge deals with impact. What are criteria of impact for research results (and other research artefacts) published on the Web? How can this be related to the publishing world appearing in print? Is a link equal to a citation or a download equal to a subscription? Can we develop a Research 2.0 specific position on impact measurement? This includes questions of authority, quality and re-evaluation of quality, and trust.The tension between openness and privacy spans the fourth challenge. The functionality of mash-ups often relies on the use of third-party services. What happens with the data, if this source is no longer available? What about hidden exchange of data among backend services?
2010

Beham Günter, Kump Barbara, Lindstaedt Stefanie , Ley Tobias

Recommending Knowledgeable People in a Work-Integrated Learning System

1st Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2010), 2010

Konferenz
According to studies into learning at work, interpersonal help seeking is the most important strategy of how people acquireknowledge at their workplaces. Finding knowledgeable persons, however, can often be difficult for several reasons. Expertfinding systems can support the process of identifying knowledgeable colleagues thus facilitating communication andcollaboration within an organization. In order to provide the expert finding functionality, an underlying user model is needed thatrepresents the characteristics of each individual user. In our article we discuss requirements for user models for the workintegratedlearning (WIL) situation. Then, we present the APOSDLE People Recommender Service which is based on anunderlying domain model, and on the APOSDLE User Model. We describe the APOSDLE People Recommender Service on thebasis of the Intuitive Domain Model of expert finding systems, and explain how this service can support interpersonal helpseeking at workplaces.
2010

Lindstaedt Stefanie , Kraker Peter, Höfler Patrick, Fessl Angela

Feeding TEL: Building an Ecosystem Around BuRST to Convey Publication Metadata

Research 2.0 Workshop EC-TEL 2010, 2010

Konferenz
In this paper we present an ecosystem for the lightweight exchangeof publication metadata based on the principles of Web 2.0. At the heart of thisecosystem, semantically enriched RSS feeds are used for dissemination. Thesefeeds are complemented by services for creation and aggregation, as well aswidgets for retrieval and visualization of publication metadata. In twoscenarios, we show how these publication feeds can benefit institutions,researchers, and the TEL community. We then present the formats, services,and widgets developed for the bootstrapping of the ecosystem. We concludewith an outline of the integration of publication feeds with the STELLARNetwork of Excellence1 and an outlook on future developments.
2010

Beham Günter, Lindstaedt Stefanie , Ley Tobias, Kump Barbara, Seifert C.

MyExperiences: Visualizing Evidence in an Open Learner Model

Adjunct Proceedings of the 18th Conference on User Modeling, Adaptation, and Personaization, Posters and Demonstrations, Bohnert, B., Quiroga, L. M., 2010

Journal
When inferring a user’s knowledge state from naturally occurringinteractions in adaptive learning systems, one has to makes complexassumptions that may be hard to understand for users. We suggestMyExperiences, an open learner model designed for these specificrequirements. MyExperiences is based on some of the key design principles ofinformation visualization to help users understand the complex information inthe learner model. It further allows users to edit their learner models in order toimprove the accuracy of the information represented there.
2010

Lindstaedt Stefanie , Rath Andreas S., Devaurs Didier

Studying the Factors Influencing Automatic User Task Detection on the Computer Desktop

Sustaining TEL: From Innovation to Learning and Practice, Lecture Notes in Computer Science, Springer, 2010

. Supporting learning activities during work has gained momentum fororganizations since work-integrated learning (WIL) has been shown to increaseproductivity of knowledge workers. WIL aims at fostering learning at the workplace,during work, for enhancing task performance. A key challenge for enablingtask-specific, contextualized, personalized learning and work support is to automaticallydetect the user’s task. In this paper we utilize our ontology-based usertask detection approach for studying the factors influencing task detection performance.We describe three laboratory experiments we have performed in twodomains including over 40 users and more than 500 recorded task executions.The insights gained from our evaluation are: (i) the J48 decision tree and Na¨ıveBayes classifiers perform best, (ii) six features can be isolated, which providegood classification accuracy, (iii) knowledge-intensive tasks can be classified aswell as routine tasks and (iv) a classifier trained by experts on standardized taskscan be used to classify users’ personal tasks.
2010

Beham Günter, Jeanquartier Fleur, Lindstaedt Stefanie

iAPOSDLE - An Approach to Mobile Work-Integrated Learning

Sustaining TEL: From Innovation to Learning and Practice, Proceedings of EC-TEL 2010, Wolpers, M., Kirschner, P. A., Scheffel, M., Lindstaedt, S. N., Dimitrova, V., Springer, 2010

Konferenz
This paper introduces iAPOSDLE, a mobile application enabling the use of work-integrated learning services without being limited by location. iAPOSDLE makes use of the APOSDLE WIL system for self-directed work-integrated learning support, and extends its range of application to mobile learning. Core features of iAPOSDLE are described and possible extensions are discussed.
2010

Ley Tobias, Seitlinger Paul

A Cognitive Perspective on Emergent Semantics in Collaborative Tagging: The Basic Level Effect

International Workshop on Adaptation in Social and Semantic Web - SASWeb 2010, Cena, F., Dattolo, A., Kleanthous, S., Tasso, C., Vallejo, D. B., Vassileva, J. , CEUR Workshop Proceedings, 2010

Konferenz
Researching the emergence of semantics in social systems needs totake into account how users process information in their cognitive system. Wereport results of an experimental study in which we examined the interactionbetween individual expertise and the basic level advantage in collaborative tagging.The basic level advantage describes availability in memory of certain preferredlevels of taxonomic abstraction when categorizing objects and has beenshown to vary with level of expertise. In the study, groups of students taggedinternet resources for a 10-week period. We measured the availability of tags inmemory with an association test and a relevance rating and found a basic leveladvantage for tags from more general as opposed to specific levels of the taxonomy.An interaction with expertise also emerged. Contrary to our expectations,groups that spent less time to develop a shared understanding shifted tomore specific levels as compared to groups that spent more time on a topic. Weattribute this to impaired collaboration in the groups. We discuss implicationsfor personalized tag and resource recommendations.
2009

Lindstaedt Stefanie , Rath Andreas S., Devaurs Didier

UICO: An Ontology-Based User Interaction Context Model for Automatic Task Detection on the Computer Desktop

Proceedings of the Context Information and Ontology (CIAO2009) workshop as part of the ESWC 2009, Gomez-Perez, J. M., Haase, P., Tilly, M., Warren, P., ACM, 2009

Konferenz
‘Understanding context is vital’ [1] and ‘context is key’ [2]signal the key interest in the context detection field. Oneimportant challenge in this area is automatically detectingthe user’s task because once it is known it is possible tosupport her better. In this paper we propose an ontologybaseduser interaction context model (UICO) that enhancesthe performance of task detection on the user’s computerdesktop. Starting from low-level contextual attention metadatacaptured from the user’s desktop, we utilize rule-based,information extraction and machine learning approaches toautomatically populate this user interaction context model.Furthermore we automatically derive relations between themodel’s entities and automatically detect the user’s task.We present evaluation results of a large-scale user study wecarried out in a knowledge-intensive business environment,which support our approach.
2009

Gras R., Devaurs Didier, Wozniak A., Aspinall A.

An Individual-Based Evolving Predator-Prey Ecosystem Simulation Using a Fuzzy Cognitive Map as the Behavior Model

Artificial Life, Massachusetts Institute of Technology, 2009

Journal
We present an individual-based predator-prey model with, for the first time, each agent behavior being modeled by a fuzzy cognitive map (FCM), allowing the evolution of the agent behavior through the epochs of the simulation. The FCM enables the agent to evaluate its environment (e.g., distance to predator or prey, distance to potential breeding partner, distance to food, energy level) and its internal states (e.g., fear, hunger, curiosity), and to choose several possible actions such as evasion, eating, or breeding. The FCM of each individual is unique and is the result of the evolutionary process. The notion of species is also implemented in such a way that species emerge from the evolving population of agents. To our knowledge, our system is the only one that allows the modeling of links between behavior patterns and speciation. The simulation produces a lot of data, including number of individuals, level of energy by individual, choice of action, age of the individuals, and average FCM associated with each species. This study investigates patterns of macroevolutionary processes, such as the emergence of species in a simulated ecosystem, and proposes a general framework for the study of specific ecological problems such as invasive species and species diversity patterns. We present promising results showing coherent behaviors of the whole simulation with the emergence of strong correlation patterns also observed in existing ecosystems.
2009

Lindstaedt Stefanie , de Hoog R., Aehnelt M.

APOSDLE: Contextualized Collaborative Knowledge Work Support

Supplementary Proceedings of the 11th European Conference on Computer Supported Cooperative Work, Demos, Videos, Posters, Vienna, Austria, 7 - 11 September 2009, 2009

Konferenz
This contribution shortly introduces the collaborative APOSDLE environmentfor integrated knowledge work and learning. It proposes a video presentation and thepresentation of the third APOSDLE prototype.
2009

Lex Elisabeth, Granitzer Michael, Juffinger A., Seifert C.

Cross-Domain Classification: Trade-Off between Complexity and Accuracy

Proceedings of the 4th International Conference for Internet Technology and Secured Transactions (ICITST) 2009, 2009

Text classification is one of the core applications in data mining due to the huge amount of not categorized digital data available. Training a text classifier generates a model that reflects the characteristics of the domain. However, if no training data is available, labeled data from a related but different domain might be exploited to perform crossdomain classification. In our work, we aim to accurately classify unlabeled blogs into commonly agreed newspaper categories using labeled data from the news domain. The labeled news and the unlabeled blog corpus are highly dynamic and hourly growing with a topic drift, so a trade-off between accuracy and performance is required. Our approach is to apply a fast novel centroid-based algorithm, the Class-Feature-Centroid Classifier (CFC), to perform efficient cross-domain classification. Experiments showed that this algorithm achieves a comparable accuracy than k-NN and is slightly better than Support Vector Machines (SVM), yet at linear time cost for training and classification. The benefit of this approach is that the linear time complexity enables us to efficiently generate an accurate classifier, reflecting the topic drift, several times per day on a huge dataset.
2009

Pellegrini T., Auer S., Schaffert S.

Networked Knowledge - Networked Media Integrating Knowledge Management, New Media Technologies and Semantic Systems

Studies in Computational Intelligence, Springer, 2009

Buch
2009

Lindstaedt Stefanie , Rospocher M., Ghidini C., Pammer-Schindler Viktoria, Serafini L.

MoKi: the Modelling wiKi

Proceedings of the Forth Semantic Wiki Workshop (SemWiki 2009), co-located with 6th European Semantic Web Conference (ESWC 2009), CEUR Workshop Proceedings, 2009

Konferenz
Enterprise modelling focuses on the construction of a structureddescription of relevant aspects of an enterprise, the so-called enterprisemodel. Within this contribution we describe a wiki-based tool forenterprise modelling, called MoKi (Modelling wiKi). It specifically facilitatescollaboration between actors with different expertise to develop anenterprise model by using structural (formal) descriptions as well as moreinformal and semi-formal descriptions of knowledge. It also supports theintegrated development of interrelated models covering different aspectsof an enterprise.
2009

Schmidt A., Hinkelmann K., Ley Tobias, Lindstaedt Stefanie , Maier R., Riss U.

Conceptual Foundations for a Service-oriented Knowledge and Learning Architecture: Supporting Content, Process, and Ontology Maturing

Networked Knowledge - Networked Media Integrating Knowledge Management, New Media Technologies and Semantic Systems, Studies in Computational Intelligence, Pellegrini, T., Auer, S., Tochtermann, K., Schaffert, S., Springer, 2009

Buch
Effective learning support in organizations requires a flexible and personalizedtoolset that brings together the individual and the organizational perspectiveon learning. Such toolsets need a service-oriented infrastructure of reusable knowledgeand learning services as an enabler. This contribution focuses on conceptualfoundations for such an infrastructure as it is being developed within the MATUREIP and builds on the knowledge maturing process model on the one hand, and theseeding-evolutionary growth-reseeding model on the other hand. These theories areused to derive maturing services, for which initial examples are presented.
2009

Schachner W., Koubek A.

Bessere Unternehmen mit Wissensmanagement?

QZ Qualität und Zuverlässigkeit, 03/2009, Carl Hanser Verlag, München, 2009

Journal
2009

Thurner-Scheuerer Claudia

Thema des Monats: Die Plattform Wissensmanagement – ein Stern im Wissensmanagement-Orbit

Community of knowledge, Rollen - Koordinator WM, 2009

Journal
2009

Granitzer Michael, Lex Elisabeth, Juffinger A.

Blog Credibility Ranking by Exploiting Verified Content

Proceedings of the 3rd Workshop on Information Credibility on the Web at 18th World Wide Web Conference, 2009

Konferenz
People use weblogs to express thoughts, present ideas and share knowledge. However, weblogs can also be misused to influence and manipulate the readers. Therefore the credibility of a blog has to be validated before the available information is used for analysis. The credibility of a blogentry is derived from the content, the credibility of the author or blog itself, respectively, and the external references or trackbacks. In this work we introduce an additional dimension to assess the credibility, namely the quantity structure. For our blog analysis system we derive the credibility therefore from two dimensions. Firstly, the quantity structure of a set of blogs and a reference corpus is compared and secondly, we analyse each separate blog content and examine the similarity with a verified news corpus. From the content similarity values we derive a ranking function. Our evaluation showed that one can sort out incredible blogs by quantity structure without deeper analysis. Besides, the content based ranking function sorts the blogs by credibility with high accuracy. Our blog analysis system is therefore capable of providing credibility levels per blog.
2009

Lindstaedt Stefanie , Ghidini C., Kump Barbara, Mahbub N., Pammer-Schindler Viktoria, Rospocher M., Serafini L.

MoKi: The Enterprise Modelling Wiki

The Semantic Web: Research and Applications, 6th European Semantic Web Conference, ESWC 2009, Heraklion, Crete, Greece, May 31-June 4, 2009, Proceedings, Aroyo, L., Traverso, P., Ciravegna, F., Cimiano, P., Heath, T., Hyvönen, E., Mizoguchi, R., Oren, E., Sabou, M. & Simperl, E. P. B., Springer, 2009

Konferenz
Enterprise modelling focuses on the construction of a structureddescription, the so-called enterprise model, which represents aspectsrelevant to the activity of an enterprise. Although it has becomeclearer recently that enterprise modelling is a collaborative activity, involvinga large number of people, most of the enterprise modelling toolsstill only support very limited degrees of collaboration. Within thiscontribution we describe a tool for enterprise modelling, called MoKi(MOdelling wiKI), which supports agile collaboration between all differentactors involved in the enterprise modelling activities. MoKi is basedon a Semantic Wiki and enables actors with different expertise to developan enterprise model not only using structural (formal) descriptions butalso adopting more informal and semi-formal descriptions of knowledge.
2009

Ley Tobias, Lindstaedt Stefanie , Schöfegger Karin, Seitlinger Paul, Weber Nicolas, Hu Bo, Riss Uwe, Brun Roman, Hinkelmann Knut, Thönssen Barbara, Maier Ronald, Schmidt Andreas

Maturing Services Definition

2009

2009

Lindstaedt Stefanie , Hambach S., Müsebeck P., de Hoog R., Kooken J., Musielak M.

Context and Scripts: Supporting Interactive Work-Integrated Learning

Computer Supported Collaboration Learning Practices, CSCL09, 8-13 June 2009, Rhodes, Greece, Dimitracopoulou A., O’Malley, C., Suthers D., Reimann P. , 2009

Journal
Computational support for work-integrated learning will gain more and moreattention. We understand informal self-directed work-integrated learning of knowledgeworkers as a by-product of their knowledge work activities and propose a conceptual as wellas a technical approach for supporting learning from documents and learning in interactionwith fellow knowledge workers. The paper focuses on contextualization and scripting as twomeans to specifically address the latter interaction type.
2009

Lex Elisabeth, Juffinger A.

Crosslanguage Blog Mining and Trend Visualisation

Proceedings of the 18th World Wide Web Conference, 2009

Konferenz
People use weblogs to express thoughts, present ideas and share knowledge, therefore weblogs are extraordinarily valuable resources, amongs others, for trend analysis. Trends are derived from the chronological sequence of blog post count per topic. The comparison with a reference corpus allows qualitative statements over identified trends. We propose a crosslanguage blog mining and trend visualisation system to analyse blogs across languages and topics. The trend visualisation facilitates the identification of trends and the comparison with the reference news article corpus. To prove the correctness of our system we computed the correlation between trends in blogs and news articles for a subset of blogs and topics. The evaluation corroborated our hypothesis of a high correlation coefficient for these subsets and therefore the correctness of our system for different languages and topics is proven.
2009

Lindstaedt Stefanie , Aehnelt M., de Hoog R.

Supporting the Learning Dimension of Knowledge Work

Learning in the Synergy of Multiple Disciplines, 4th European Conference on Technology Enhanced Learning, EC-TEL 2009, Nice, France, September 29 - October 2, 2009, Cress, U., Dimitrova, V., Specht, M., 2009

Konferenz
2009

Pammer-Schindler Viktoria, Serafini L., Lindstaedt Stefanie

Highlighting assertional effects of ontology editing activities in OWL

Proceedings of the 3rd International Workshop on Ontology Dynamics, (IWOD 2009), collocated with the 8th International Semantic Web Conference (ISWC-2009), d'Acquin, M., Antoniou, G., CEUR Workshop Proceedings, 2009

Konferenz
2009

Lindstaedt Stefanie , Moerzinger R., Sorschag R. , Pammer-Schindler Viktoria, Thallinger G.

Automatic Image Annotation using Visual Conent and Folksonomies

Multimedia Tools and Applications, Springer US, 2009

Journal
Automatic image annotation is an important and challenging task, andbecomes increasingly necessary when managing large image collections. This paperdescribes techniques for automatic image annotation that take advantage of collaborativelyannotated image databases, so called visual folksonomies. Our approachapplies two techniques based on image analysis: First, classification annotates imageswith a controlled vocabulary and second tag propagation along visually similar images.The latter propagates user generated, folksonomic annotations and is thereforecapable of dealing with an unlimited vocabulary. Experiments with a pool of Flickrimages demonstrate the high accuracy and efficiency of the proposed methods in thetask of automatic image annotation. Both techniques were applied in the prototypicaltag recommender “tagr”.
2009

Neidhart T., Granitzer Michael, Kern Roman, Weichselbraun A., Wohlgenannt G., Scharl A., Juffinger A.

Distributed Web2.0 Crawling for Ontology Evolution

Journal of Digital Information Management, 2009

Journal
2009

Latif A., Afzal M. T., Höfler Patrick, Us Saeed A.

Turning Keywords into URIs: Simplified User Interfaces for Exploring Linked Data

ACM Proceeding of ICIS 2009. ISBN: 978-1-60558-710-3, 2009

Konferenz
The Semantic Web strives to add structure and meaning to the Web, thereby providing better results and easier interfaces for its users. One important foundation of the Semantic Web is Linked Data, the concept of interconnected data, describing resources by use of RDF and URIs. Linked Data (LOD) provides the opportunity to explore and combine datasets on a global scale -- something which has never been possible before. However, at its current stage, the Linked Data cloud yields little benefit for end users who know nothing of ontologies, triples and SPARQL. This paper presents an intelligent technique for locating desired URIs from the huge repository of Linked Data. Search keywords provided by users are utilized intelligently for locating the intended URI. The proposed technique has been applied in a simplified end user interface for LOD. The system evaluation shows that the proposed technique has reduced user's cognitive load in finding relevant information.
2009

Granitzer Michael, Rath Andreas S., Kröll Mark, Ipsmiller D., Devaurs Didier, Weber Nicolas, Lindstaedt Stefanie , Seifert C.

Machine Learning based Work Task Classification

Journal of Digital Information Management, 2009

Journal
Increasing the productivity of a knowledgeworker via intelligent applications requires the identification ofa user’s current work task, i.e. the current work context a userresides in. In this work we present and evaluate machine learningbased work task detection methods. By viewing a work taskas sequence of digital interaction patterns of mouse clicks andkey strokes, we present (i) a methodology for recording thoseuser interactions and (ii) an in-depth analysis of supervised classificationmodels for classifying work tasks in two different scenarios:a task centric scenario and a user centric scenario. Weanalyze different supervised classification models, feature typesand feature selection methods on a laboratory as well as a realworld data set. Results show satisfiable accuracy and high useracceptance by using relatively simple types of features.
2009

Beham Günter, Lindstaedt Stefanie , Kump Barbara, Resanovic D.

Non-invasive User Modeling for Recommending Knowledgeable Persons in Work-integrated Learning

Second Stellar Alpine Rendez-Vous 2009, Workshop for Context-aware recommendation for learning, STELLAR, 2009

Konferenz
2009

Schoefegger K., Weber Nicolas, Lindstaedt Stefanie , Ley Tobias

KNOWLEDGE MATURING SERVICES: Supporting Knowledge Maturing in Organisational Environments

Knowledge Science, Engineering and Management, Third International Conference, KSEM 2009, Karagiannis, D., Jinpeng, Z., Springer, 2009

Konferenz
The changes in the dynamics of the economy and thecorresponding mobility and fluctuations of knowledge workers within organizationsmake continuous social learning an essential factor for an organization.Within the underlying organizational processes, KnowledgeMaturing refers to the the corresponding evolutionary process in whichknowledge objects are transformed from informal and highly contextualizedartifacts into explicitly linked and formalized learning objects.In this work, we will introduce a definition of Knowledge (Maturing)Services and will present a collection of sample services that can be dividedinto service functionality classes supporting Knowledge Maturingin content networks. Furthermore, we developed an application of thesesample services, a demonstrator which supports quality assurance withina highly content based organisational context.
2009

Afzal M. T., Latif A., Us Saeed A., Sturm P., Aslam S., Andrews K., Maurer H.

Discovery and Visualization of Expertise in a Scientific Community

Proceedings of the International Conference on Frontiers of Information Technology (FIT 2009), 2009

Konferenz
In numerous contexts and environments, it is necessary to identify and assign (potential) experts to subject fields. In the context of an academic journal for computer science (J.UCS), papers and reviewers are classified using the ACM classification scheme. This paper describes a system to identify and present potential reviewers for each category from the entire body of paper’s authors. The topical classification hierarchy is visualized as a hyperbolic tree and currently assigned reviewers are listed for a selected node (computer science category). In addition, a spiral visualization is used to overlay a ranked list of further potential reviewers (high-profile authors) around the currently selected category. This new interface eases the task of journal editors in finding and assigning reviewers. The system is also useful for users who want to find research collaborators in specific research areas.
2009

Weber Nicolas, Ley Tobias, Lindstaedt Stefanie , Schoefegger K., Bimrose J., Brown A., Barnes S.

Knowledge Maturing in the Semantic MediaWiki: A design study in career guidance

Lecture Notes in Computer Science 5794, Cress, U., Dimitrova, V., Specht, M., Springer, 2009

Konferenz
2009

Lindstaedt Stefanie , Beham Günter, Ley Tobias, Kump Barbara

Getting to Know Your User: Unobtrusive User Model Maintenance within Work-Integrated Learning Environments

Learning in the Synergy of Multiple Disciplines: Proceedings of the 4th European Conference on Technology Enhanced Learning, ECTEL 2009, Nice, France, September/October 2009 , Cress, U., Dimitrova, V., Specht, M., Springer, 2009

Konferenz
Work-integrated learning (WIL) poses unique challenges for usermodel design: on the one hand users’ knowledge levels need to be determinedbased on their work activities – testing is not a viable option; on the other handusers do interact with a multitude of different work applications – there is nocentral learning system. This contribution introduces a user model and correspondingservices (based on SOA) geared to enable unobtrusive adaptabilitywithin WIL environments. Our hybrid user model services interpret usage datain the context of enterprise models (semantic approaches) and utilize heuristics(scruffy approaches) in order to determine knowledge levels, identify subjectmatter experts, etc. We give an overview of different types of user model services(logging, production, inference, control), provide a reference implementationwithin the APOSDLE project, and discuss early evaluation results.
2008

Scharl A., Stern Hermann, Weichselbraun A.

Annotating and Visualizing Location Data in Geospatial Web Applications

17th International World Wide Web Conference (WWW-2008), Proceedings of the First International Workshop on Location and the Web (LocWeb 2008), 2008

Konferenz
This paper presents the IDIOM Media Watch on Climate Change (www.ecoresearch.net/climate), a prototypical implementation of an environmental portal that emphasizes the importance of location data for advanced Web applications. The introductory section outlines the process of retrofitting existing knowledge repositories with geographical context information, a process also referred to as geotagging. The paper then describes the portal’s functionality, which aggregates, annotates and visualizes environmental articles from 150 Anglo-American news media sites. From 300,000 news media articles gathered in weekly intervals, the system selects about 10,000 focusing on environmental issues. The crawled data is indexed and stored in a central repository. Geographic location represents a central aspect of the application, but not the only dimension used to organize and filter content. Applying the concepts of location and topography to semantic similarity, the paper concludes with discussing information landscapes as alternative interface metaphor for accessing large Web repositories.
2008

Sabol Vedran, Scharl A.

Visualizing Temporal-Semantic Relations in Dynamic Information Landscapes

GeoVisualization of Dynamics, Movement and Change Workshop at the AGILE 2008 Conference, Spain, 2008

Konferenz
2008

Lex Elisabeth, Kienreich Wolfgang, Granitzer Michael, Seifert C.

A generic framework for visualizing the news article domain and its application to real-world data

Journal of Digital Information Management, 2008

Journal
2008

Ley Tobias, Kump Barbara, Ulbrich Armin, Scheir Peter, Lindstaedt Stefanie

A Competence-based Approach for Formalizing Learning Goals in Work-integrated Learning

Proceedings of the ED-Media 2008, Vienna, Austria, June 30-July 4, 2008,, Chesapeake, VA: AACE, 2008

Konferenz
The paper suggests a way to support work-integrated learning for knowledge workwhich poses a great challenge for current research and practice. We first present a WorkplaceLearning Context Model which has been derived by analyzing knowledge work and the knowledgesources used by knowledge workers. The model specifies an integrative view on knowledgeworkers’ work environment by connecting learning, work and knowledge spaces. We then focuson the part of the context which specifies learning goals and their interrelations to task and domainmodels. Our purpose is to support learning needs analysis which is based on a comparison of tasksperformed in the past to those tasks to be tackled in the future. A first implementation in theAPOSDLE project is presented including the models generated for five real world applications andthe software prototype. We close with an outlook on future work.
2008

Lindstaedt Stefanie , Ley Tobias, Scheir Peter, Ulbrich Armin

Applying Scruffy Methods to Enable Work-integrated Learning

The European Journal of the Informatics Professional, 2008

Journal
This contribution introduces the concept of work-integrated learning which distinguishes itself from traditional e-Learning in that it provides learning support (i) during work task execution and tightly contextualized to the work context,(ii) within the work environment, and (iii) utilizes knowledge artefacts available within the organizational memory for learning. We argue that in order to achieve this highly flexible learning support we need to turn to" scruffy" methods (such as associative retrieval, genetic algorithms, Bayesian and other probabilistic methods) which can provide good results in the presence of uncertainty and the absence of fine-granular models. Hybrid approaches to user context determination, user profile management, and learning material identification are discussed and first results are reported.
2008

Granitzer Michael, Granitzer Gisela, Lindstaedt Stefanie , Rath Andreas S., Groiss W.

Automating Knowledge Transfer and Creation in Knowledge Intensive Business Processes

Proceedings of the First Workshop on Business Process Management and Social Software BPMS2 08, September 1, 2008, Mailand, Italien, Springer, 2008

It is a well known fact that a wealth of knowledge lies in thehead of employees making them one of the most or even the most valuableasset of organisations. But often this knowledge is not documented andorganised in knowledge systems as required by the organisation, butinformally shared. Of course this is against the organisation’s aim forkeeping knowledge reusable as well as easily and permanently availableindependent of individual knowledge workers.In this contribution we suggest a solution which captures the collectiveknowledge to the benefit of the organisation and the knowledge worker.By automatically identifying activity patterns and aggregating them totasks as well as by assigning resources to these tasks, our proposed solutionfulfils the organisation’s need for documentation and structuring ofknowledge work. On the other hand it fulfils the the knowledge worker’sneed for relevant, currently needed knowledge, by automatically miningthe entire corporate knowledge base and providing relevant, contextdependent information based on his/her current task.
2008

Granitzer Michael, Kröll Mark, Seifer Christin, Rath Andreas S., Weber Nicolas, Dietzel O., Lindstaedt Stefanie

Analysis of Machine Learning Techniques for Context Extraction

Proceedings of 2008 International Conference on Digital Information Management (ICDIM08), IEEE Computer Society Press, 2008

Konferenz
’Context is key’ conveys the importance of capturing thedigital environment of a knowledge worker. Knowing theuser’s context offers various possibilities for support, likefor example enhancing information delivery or providingwork guidance. Hence, user interactions have to be aggregatedand mapped to predefined task categories. Withoutmachine learning tools, such an assignment has to be donemanually. The identification of suitable machine learningalgorithms is necessary in order to ensure accurate andtimely classification of the user’s context without inducingadditional workload.This paper provides a methodology for recording user interactionsand an analysis of supervised classification models,feature types and feature selection for automatically detectingthe current task and context of a user. Our analysisis based on a real world data set and shows the applicabilityof machine learning techniques.
2008

Lindstaedt Stefanie , Pammer-Schindler Viktoria, Mörzinger Roland, Kern Roman, Mülner Helmut, Wagner Claudia

Recommending tags for pictures based on text, visual content and user context

Third International Conference on Internet and Web Applications and Services (ICIW), IEEE, 2008

Konferenz
Imagine you are member of an online social systemand want to upload a picture into the community pool. In currentsocial software systems, you can probably tag your photo, shareit or send it to a photo printing service and multiple other stuff.The system creates around you a space full of pictures, otherinteresting content (descriptions, comments) and full of users aswell. The one thing current systems do not do, is understandwhat your pictures are about.We present here a collection of functionalities that make a stepin that direction when put together to be consumed by a tagrecommendation system for pictures. We use the data richnessinherent in social online environments for recommending tags byanalysing different aspects of the same data (text, visual contentand user context). We also give an assessment of the quality ofthus recommended tags.
2008

Ley Tobias, Ulbrich Armin, Lindstaedt Stefanie , Scheir Peter, Kump Barbara, Albert Dietrich

Modeling competencies for supporting work-integrated learning in knowledge work

Journal of knowledge management, Emerald Group Publishing Limited, 2008

Journal
Purpose – The purpose of this paper is to suggest a way to support work-integrated learning forknowledge work, which poses a great challenge for current research and practice.Design/methodology/approach – The authors first suggest a workplace learning context model, whichhas been derived by analyzing knowledge work and the knowledge sources used by knowledgeworkers. The authors then focus on the part of the context that specifies competencies by applying thecompetence performance approach, a formal framework developed in cognitive psychology. From theformal framework, a methodology is then derived of how to model competence and performance in theworkplace. The methodology is tested in a case study for the learning domain of requirementsengineering.Findings – The Workplace Learning Context Model specifies an integrative view on knowledge workers’work environment by connecting learning, work and knowledge spaces. The competence performanceapproach suggests that human competencies be formalized with a strong connection to workplaceperformance (i.e. the tasks performed by the knowledge worker). As a result, competency diagnosisand competency gap analysis can be embedded into the normal working tasks and learninginterventions can be offered accordingly. The results of the case study indicate that experts weregenerally in moderate to high agreement when assigning competencies to tasks.Research limitations/implications – The model needs to be evaluated with regard to the learningoutcomes in order to test whether the learning interventions offered benefit the user. Also, the validityand efficiency of competency diagnosis need to be compared to other standard practices incompetency management.Practical implications – Use of competence performance structures within organizational settings hasthe potential to more closely relate the diagnosis of competency needs to actual work tasks, and toembed it into work processes.Originality/value – The paper connects the latest research in cognitive psychology and in thebehavioural sciences with a formal approach that makes it appropriate for integration intotechnology-enhanced learning environments.Keywords Competences, Learning, Workplace learning, Knowledge managementPaper type Research paper
2008

Lindstaedt Stefanie , , , Lokaiczyk R., Kump Barbara, Beham Günter, Pammer-Schindler Viktoria

Knowledge Services for Work-integrated Learning

Proceedings of the European Conference on Technology Enhanced Learning (ECTEL) 2008, Maastricht, The Netherlands, September 16-19, Springer, 2008

Konferenz
In order to support work-integrated learning scenarios task- andcompetency-aware knowledge services are needed. In this paper we introducethree key knowledge services of the APOSDLE system and illustrate how theyinteract. The context determination daemon observes user interactions andinfers the current work task of the user. The user profile service uses theidentified work tasks to determine the competences of the user. And finally, theassociative retrieval service utilizes both the current work task and the inferredcompetences to identify relevant (learning) content. All of these knowledgeservices improve through user feedback.
2008

Granitzer Michael

KnowMiner - Konzeption und Entwicklung eines generischen Wissenserschließungsframeworks

Vdm Verlag Dr. Mueller (April 2008), 2008

Buch
2008

Zinnen A., Hambach S., Faatz A., Lindstaedt Stefanie , Beham Günter, Godehardt E., Goertz M., Lokaiczyk R.

Datenschutzfragen bei der Etablierung einer Arbeitsprozess-integrierten e-Learning-Lösung

Gesellschaft für Informatik (GI): DeLFI 2008 : Die 6. e-Learning Fachtagung Informatik der Gesellschaft für Informatik e.V.Bonn : Köllen, 2008, Seehusen, S., Gesellschaft für Informatink eV, 2008

Konferenz
2008

Ulbrich Armin, Höfler Patrick, Lindstaedt Stefanie

Modellierung von Anwenderverhalten im Social Semantic Web

Social Semantic Web. Web 2.0 - Was nun?, Blumauer, A., Pellegrini, T., Springer, 2008

Buch
Ziel dieses Kapitels ist es, gemeinsame Verwendungsszenariendes Semantic Web und des Social Web zu identifizieren und zu benennen.Dabei wird ein Teilaspekt des Themengebiets im Detail betrachtet: die Nutzungvon Services, die Beobachtungen des Verhaltens von Anwendern analysieren, umdaraus maschinell interpretierbare Informationen zu erhalten und diese als Modellezu organisieren. Es werden zunächst einige Eigenschaften und Unterscheidungsmerkmalevon Anwenderverhalten und organisierten Modellen dargestellt.Anschließend wird der mögliche wechselseitige Nutzen von Anwenderverhaltenund Modellen diskutiert. Den Abschluss bildet eine Betrachtung einiger exemplarischerSoftware-Services, die heute schon verwendet werden, um Anwenderverhaltenin Modelle überzuführen.
2008

Granitzer Michael, Lux M., Spaniol M.

Multimedia Semantics - The Role of Metadata

Studies in Computational Intelligence , Vol. 101, Springer, Berlin, 2008

Buch
2008

Aehnelt M., Ebert M., Beham Günter, Lindstaedt Stefanie , Paschen A.

A Socio-Technical Approach towards Supporting Intra-Organizational Collaboration

Proceedings of the European Conference on Technology Enhanced Learning (ECTEL) 2008, Maastricht, The Netherlands, September 16-19, Springer, 2008

Konferenz
Knowledge work in companies is increasingly carried out by teams of knowledge workers. They interact within and between teams with the common goal to acquire, apply, create and share knowledge. In this paper we propose a socio-technical model to support intra-organizational collaboration which specifically takes into account the social and collaborative nature of knowledge work. Our aim is to support in particular the efficiency of collaborative knowledge work processes through an automated recommendation of collaboration partners and collaboration media. We report on the theoretical as well as practical aspects of such a socio-technical model.
2008

Christl C., Ghidini C. , Guss J., Lindstaedt Stefanie , Pammer-Schindler Viktoria, Scheir Peter, Serafini L.

Deploying semantic web technologies for work integrated learning in industry. A comparison: SME vs. large sized company

Proceedings of the ISWC 2008, 7th International Semantic Web Conference, Karlsruhe, Germany, Oct 26-30, 2008 , Springer, 2008

Konferenz
Modern businesses operate in a rapidly changing environment.Continuous learning is an essential ingredient in order to stay competitivein such environments. The APOSDLE system utilizes semanticweb technologies to create a generic system for supporting knowledgeworkers in different domains to learnwork. Since APOSDLE relies onthree interconnected semantic models to achieve this goal, the questionon how to efficiently create high-quality semantic models has become oneof the major research challenges. On the basis of two concrete examplesnamelydeployment of such a learning system at EADS, a large corporation,and deployment at ISN, a network of SMEs-we report in detail theissues a company has to face, when it wants to deploy a modern learningenvironment relying on semantic web technology.
2008

Granitzer Gisela, Höfler Patrick

Learning With Social Semantic Technologies - Exploiting Latest Tools

International Journal of Emerging Technologies in Learning (iJET), Vol 3, 2008

Journal
Even though it was only about three years ago that Social Software became a trend, it has become a common practice to utilize Social Software in learning institutions. It brought about a lot of advantages, but also challenges. Amounts of distributed and often unstructured user generated content make it difficult to meaningfully process and find relevant information. According to the estimate of the authors, the solution lies in underpinning Social Software with structure resulting in Social Semantic Software. In this contribution we introduce the central concepts Social Software, Semantic Web and Social Semantic Web and show how Social Semantic Technologies might be utilized in the higher education context.
2008

Jones S., Lynch P., Maiden N., Lindstaedt Stefanie

Use and Influence of Creative Ideas and Requirements for a Work-Integrated Learning System

proceedings of the RE08, 16th International Requirements Engineering Conference, Barcelona, Catalunya, Spain, September 8-12, IEEE Computer Society Press, 2008

Konferenz
In this paper, we describe a creativity workshop thatwas used in a large research project, called APOSDLE,to generate creative ideas and requirements for a workintegratedlearning system. We present an analysis ofempirical data collected during and after the workshop.On the basis of this analysis, we conclude that the workshopwas an efficient way of generating ideas for futuresystem development. These ideas, on average, were usedat least as much as requirements from other sources inwriting use cases, and 18 months after the workshop wereseen to have a similar degree of influence on the projectto other requirements. We make some observations aboutthe use of more and less creative ideas, and about thetechniques used to generate them. We end with suggestionsfor further work.
2008

Stocker A., Höfler Patrick, Granitzer Gisela, Willfort R., Köck Anna Maria, Pammer-Schindler Viktoria

Towards a Framework for Social Web Platforms: The Neurovation Case

Proceedings of the Third International Conference on Internet and Web Applications and Services (ICIW 2008), IEEE Computer Society Press, 2008

Konferenz
Social web platforms have become very popular in the so-called Web 2.0, and there is no end in sight. However, very few systematic models for the constitution of such sociotechnical infrastructures exist in the scientific literature. We therefore present a generic framework for building social web platforms based on the creation of value for individuals, communities and social networks. We applied this framework in the Neurovation project, aiming to establish a platform for creative knowledge workers. This paper describes work in progress and the lessons we have learned so far.
2008

Strohmaier M., Horkoff Jennifer, Yu E., Aranda Jorge, Easterbrook Steve

Can Patterns improve Modeling? Two Exploratory Studies

International Working Conference on Requirements Engineering: Foundations for Software Quality (REFSQ'08), co-located with CAISE'08, Montpellier, France, 2008, 2008

Konferenz
A considerable amount of effort has been placed into the investigation of i* modeling as a tool for early stage requirements engineering. However, widespread adoption of i* models in the requirements process has been hindered by issues such as the effort required to create the models, coverage of the problem context, and model complexity. In this work, we explore the feasibility of pattern application to address these issues. To this end, we perform both an exploratory case study and initial experiment to investigate whether the application of patterns improves aspects of i* modeling. Furthermore, we develop a methodology which guides the adoption of patterns for i* modeling. Our findings suggest that applying model patterns can increase model coverage, but increases complexity, and may increase modeling effort depending on the experience of the modeler. Our conclusions indicate situations where pattern application to i* models may be beneficial.