Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

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

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

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

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

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

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

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

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.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reiter-Haas Markus, Slawicek Valentin, Lacic Emanuel

Studo Jobs: Enriching Data With Predicted Job Labels

ACM, Graz, 2017

Konferenz
2017

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

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

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

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

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

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.
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