Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

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

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

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

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

Trattner Christoph, Kuśmierczyk Tomasz, Nørvåg Kjetil

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

ERCIM NEWS, ERCIM EEIG, 2016

Journal
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

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

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

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

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

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

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

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.
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