Krajnc Aleksandra, Iacono Lucas, Kirschbichler Stephan, Klein Christoph, Breitfuss D, Steidl T, Pucher J
2023
This study investigates the kinematics of vehicle occupants on the passenger seat in reclined and upright seated positions. Thirty-nine volunteers (12 female and 27 male) were tested in 30 kph and 50 kph braking and steering manoeuvres. Eleven manoeuvres were conducted with each volunteer in aware and unaware states. A sedan modified with a belt integrated seat was used. The kinematics was recorded with a video-based system and (additionally) with acceleration / angular velocity sensors. Interaction with the seat was measured with pressure mats and the muscle activity was recorded in the upper body and in the lower body muscles. This publication focuses on the occupant kinematics and its processing with linear mathematical model. Kinematics and respective corridors are predicted for certain age, gender, and anthropometric data.
Geiger Bernhard, Kubin Gernot
2021
This Special Issue aims to investigate the properties of the information bottleneck (IB) functional in its new context in deep learning and to propose learning mechanisms inspired by the IB framework. More specifically, we invited authors to submit manuscripts that provide novel insight into the properties of the IB functional that apply the IB principle for training deep, i.e., multi-layer machine learning structures such as NNs and that investigate the learning behavior of NNs using the IBframework. To cover the breadth of the current literature, we also solicited manuscripts that discuss frameworks inspired by the IB principle, but that depart from them in a well-motivated manner.
Kraus Pavel, Bornemann Manfred, Alwert Kay, Matern, Andreas, Reimer, Ulrich, Kaiser Rene_DB
2020
Wissensmanagement (WM) hatte bis 2007 keinen allgemein gleich verstandenen Begriffs- und Definitionsunterbau. Gerade in wirtschaftlich schwierigen Zeiten muss WM als Disziplin für seine eigene Klarheit und Stringenz sorgen – eine Zersplitterung in verschiedene Denkschulen schwächt WM-Kommunikation, -Einsatz und -Weiterentwicklung. Das DACH-WM-Glossar erscheint in einer neuen Form und zwar aus einer pragmatischen Synthese der Glossare Praxishandbuch des WM-Forums Graz von 2007 und des DACH-WM-Glossars von 2009, ergänzt durch zusätzliche Quellen.
Kern Roman, Al-Ubaidi Tarek, Sabol Vedran, Krebs Sarah, Khodachenko Maxim, Scherf Manuel
2020
Scientific progress in the area of machine learning, in particular advances in deep learning, have led to an increase in interest in eScience and related fields. While such methods achieve great results, an in-depth understanding of these new technologies and concepts is still often lacking and domain knowledge and subject matter expertise play an important role. In regard to space science there are a vast variety of application areas, in particular with regard to analysis of observational data. This chapter aims at introducing a number of promising approaches to analyze time series data, via the introduction query by example, i.e., any signal can be provided to the system, which then responds with a ranked list of datasets containing similar signals. Building on top of this ability the system can then be trained using annotations provided by expert users, with the goal of detecting similar features and hence provide a semiautomated analysis and classification. A prototype built to work on MESSENGER data based on existing background implementations by the Know-Center in cooperation with the Space Research Institute in Graz is presented. Further, several representations of time series data that demonstrated to be required for analysis tasks, as well as techniques for preprocessing, frequent pattern mining, outlier detection, and classification of segmented and unsegmented data, are discussed. Screen shots of the developed prototype, detailing various techniques for the presentation of signals, complete the discussion.
Chiancone Alessandro, Cuder Gerald, Geiger Bernhard, Harzl Annemarie, Tanzer Thomas, Kern Roman
2019
This paper presents a hybrid model for the prediction of magnetostriction in power transformers by leveraging the strengths of a data-driven approach and a physics-based model. Specifically, a non-linear physics-based model for magnetostriction as a function of the magnetic field is employed, the parameters of which are estimated as linear combinations of electrical coil measurements and coil dimensions. The model is validated in a practical scenario with coil data from two different suppliers, showing that the proposed approach captures the different magnetostrictive properties of the two suppliers and provides an estimation of magnetostriction in agreement with the measurement system in place. It is argued that the combination of a non-linear physics-based model with few parameters and a linear data-driven model to estimate these parameters is attractive both in terms of model accuracy and because it allows training the data-driven part with comparably small datasets.
Lassnig Markus, Stabauer Petra, Breitfuß Gert, Müller Julian
2019
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.
Kaiser Rene_DB
2019
Video content and technology is an integral part of our private and professional lives. We consume news and entertainment content, and besides communication and learning there are many more significant application areas. One area, however, where video content and technology is not (yet) utilized and exploited to a large extent are production environments in factories of the producing industries like the semiconductor and electronic components and systems (ECS) industries. This article outlines some of the opportunities and challenges towards better exploitation of video content and technology in such contexts. An understanding of the current situation is the basis for future socio-technical interventions where video technology may be integrated in work processes within factories.
Fessl Angela, Wesiak Gudrun, Pammer-Schindler Viktoria
2018
Managing knowledge in periods of digital change requires not only changes in learning processes but also in knowledge transfer. For this knowledge transfer, we see reflective learning as an important strategy to keep the vast body of theoretical knowledge fresh and up-to-date, and to transfer 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 an 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 into practice. The results show that by playful learning and presenting reflective questions at the right time, participants reflected and related theoretical knowledge to practical experience.
Geiger Bernhard
2018
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.
Geiger Bernhard
2018
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.
Kowald Dominik
2017
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
Breitfuß Gert, Kaiser Rene_DB, Kern Roman, Kowald Dominik, Lex Elisabeth, Pammer-Schindler Viktoria, Veas Eduardo Enrique
2017
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.
Lindstaedt Stefanie , Czech Paul, Fessl Angela
2017
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.
Meixner Britta, Kaiser Rene_DB, Jäger Joscha, Ooi Wei Tsang, Kosch Harald
2017
(journal special issue)
Ginthör Robert, Lamb Reinhold, Koinegg Johann
2017
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.
Atzmüller Martin, Alvin Chin, Trattner Christoph
2016
Trattner Christoph, Schäfer Hanna, Said Alan, Ludwig Bernd, Elsweiler David
2016
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:
Atzmüller Martin, Chin Alvin, Trattner Christoph
2016
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.
Lindstaedt Stefanie , Ley Tobias, Sack Harald
2015
Kravcik Milos, Mikroyannidis Alexander, Pammer-Schindler Viktoria, Prilla Michael , Ullmann T.D.
2015
Silva Nelson, Eggeling Eva, Schreck Tobias, Fellner Dieter W.
2015
Lacic Emanuel, Kowald Dominik, Eberhard Lukas, Trattner Christoph, Parra Denis, Marinho Leandro
2015
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.
Kowald Dominik, Seitlinger Paul, Kopeinik Simone, Ley Tobias, Trattner Christoph
2015
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.
Kowald Dominik, Kopeinik S., Seitlinger Paul, Trattner Christoph, Ley Tobias
2015
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.
Stegmaier Florian, Seifert Christin, Kern Roman, Höfler Patrick, Bayerl Sebastian, Granitzer Michael, Kosch Harald, Lindstaedt Stefanie , Mutlu Belgin, Sabol Vedran, Schlegel Kai
2014
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.
Ullrich Torsten, Silva Nelson, Eggeling Eva, Fellner Dieter W.
2014
Ullrich Torsten, Silva Nelson, Eggeling Eva, Fellner Dieter W.
2013
Ullrich Torsten, Silva Nelson, Eggeling Eva, Fellner Dieter W.
2013
Seitlinger Christian, Schöfegger Karin, Lindstaedt Stefanie , Ley Tobias
2012
Ravenscroft Andrew, Lindstaedt Stefanie , Delgado Kloos Carlos, Hernández-Leo Davinia
2012
This book constitutes the refereed proceedings of the 7th European Conference on Technology Enhanced Learning, EC-TEL 2012, held in Saarbrücken, Germany, in September 2012. The 26 revised full papers presented were carefully reviewed and selected from 130 submissions. The book also includes 12 short papers, 16 demonstration papers, 11 poster papers, and 1 invited paper. Specifically, the programme and organizing structure was formed through the themes: mobile learning and context; serious and educational games; collaborative learning; organisational and workplace learning; learning analytics and retrieval; personalised and adaptive learning; learning environments; academic learning and context; and, learning facilitation by semantic means.
Lindstaedt Stefanie , Christl Conny
2011
This chapter presents a domain-independent computational environment which supports work-integrated learning at the professional workplace. The Advanced Process-Oriented Self-Directed Learning Environment (APOSDLE) provides 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 repurposes content which was not originally intended for learning (instead of relying on the expensive manual creation of learning material). Since this definition of work-integrated learning might differ from other definitions employed within this book, a short summary of the theoretical background is provided. Along the example of the company Innovation Service Network (ISN), a network of SME’s, a rich and practical description of the deployment and usage of APOSDLE is given. The chapter provides the reader with firsthand experiences and discusses efforts and lessons learned, backed up with experiences gained in two other application settings, namely EADS in France and a Chamber of Commerce and industry in Germany.
Scheir Peter, Prettenhofer Peter, Lindstaedt Stefanie , Ghidini Chiara
2010
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.
Wolpers Martin, Kirschner Paul A., Scheffel Maren, Lindstaedt Stefanie , Dimitrova Vania
2010
Lindstaedt Stefanie , Duval E., Ullmann T.D., Wild F., Scott P.
2010
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?
Schachner W.
2010
Schachner W.
2010
Schachner W.
2010
Kröll Mark, Prettenhofer P., Strohmaier M.
2009
Access to knowledge about user goals represents a critical component for realizing the vision of intelligent agents acting upon user intent on the web. Yet, the manual acquisition of knowledge about user goals is costly and often infeasible. In a departure from existing approaches, this paper proposes Goal Mining as a novel perspective for knowledge acquisition. The research presented in this chapter makes the following contributions: (a) it presents Goal Mining as an emerging field of research and a corresponding automatic method for the acquisition of user goals from web corpora, in the case of this paper search query logs (b) it provides insights into the nature and some characteristics of these goals and (c) it shows that the goals acquired from query logs exhibit traits of a long tail distribution, thereby providing access to a broad range of user goals. Our results suggest that search query logs represent a viable, yet largely untapped resource for acquiring knowledge about explicit user goals
Pellegrini T., Auer S., Schaffert S.
2009
Schmidt A., Hinkelmann K., Ley Tobias, Lindstaedt Stefanie , Maier R., Riss U.
2009
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.
Granitzer Michael, Lux M., Spaniol M.
2008
Ulbrich Armin, Höfler Patrick, Lindstaedt Stefanie
2008
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.
Granitzer Michael
2008
Lindstaedt Stefanie , Ulbrich Armin
2006
Lindstaedt Stefanie , Ley Tobias, Farmer Johannes
2004
2004
2004
Maurer H.
2004
Ley Tobias
2004
Hrastnik J., Rollett H., Strohmaier M.
2004
Lindstaedt Stefanie , Farmer J., Ley Tobias
2004
2003
Maurer H.
2003
2002
Maurer H.
2002
2002