Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2012

Devaurs Didier, Rath Andreas S., Lindstaedt Stefanie

Exploiting the User Interaction Context for Automatic Task Detection

Applied Artificial Intelligence, Taylor & Francis Group, 2012

Journal
Detecting the task a user is performing on his/her computer desktop is important in order to provide him/her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (1) capturing simple user interaction events on the computer desktop and (2) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task-detection performance. We also argue that good results can be achieved by training task classifiers “offline” on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones.
2011

Lindstaedt Stefanie , Kump Barbara, Rath Andreas S.

Context-Aware Recommendation for Work-Integrated Learning

Context and Semantics for Knowledge Management. Technologies for Personal Productivity, Warren, P., Davies, J., Simperl, E., Springer, 2011

Journal
Within this chapter we first outline the important role learning plays within knowledge work and its impact on productivity. As a theoretical background we introduce the paradigm of Work-Integrated Learning (WIL) which conceptualizes informal learning at the workplace and takes place tightly intertwined with the execution of work tasks. Based on a variety of in-depth knowledge work studies we identify key requirements for the design of work-integrated learning support. Our focus is on providing learning support during the execution of work tasks (instead of beforehand), within the work environment of the user (instead of within a separate learning system), and by repurposing content for learning which was not originally intended for learning (instead of relying on the expensive manual creation of learning material). In order to satisfy these requirements we developed a number of context-aware knowledge services. These services integrate semantic technologies with statistical approaches which perform well in the face of uncertainty. These hybrid knowledge services include the automatic detection of a user’s work task, the ‘inference’ of the user’s competencies based on her past activities, context-aware recommendation of content and colleagues, learning opportunities, etc. A summary of a 3 month in-depth summative workplace evaluation at three testbed sites concludes the chapter.
2011

Rath Andreas S., Devaurs Didier, Lindstaedt Stefanie

An Ontology-Based Approach for Detecting Knowledge Intensive Tasks

Journal of Digital Information Management, Pichappan, P., Jacobs, D. , Digital Information Research Foundation, 2011

Journal
In the context detection field, an important challenge is automatically detecting the user's task, for providing contextualized and personalized user support. Several approaches have been proposed to perform task classification, all advocating the window title as the best discriminative feature. In this paper we present a new ontology-based task detection approach, and evaluate it against previous work. We show that knowledge intensive tasks cannot be accurately classified using only the window title. We argue that our approach allows classifying such tasks better, by providing feature combinations that can adapt to the domain and the degree of freedom in task execution.
2010

Pozzi, Francesca, Persico, Donatella, Fischer, Frank, Hofmann, Lena, Lindstaedt Stefanie , Cress, Ulrike, Rath Andreas S., Moskaliuk, Johannes, Weber, Nicolas, Kimmerle, Joachim, Devaurs Didier, Ney, Muriel, Gonçalves, Celso, Balacheff, Nicolas, Schwartz, Claudine, Bosson, Jean-Luc, Dillenbourg, Pierre, Jermann, Patrick, Zufferey, Guillaume, Brown, Elisabeth, Sharples, Mike, Windrum, Caroline, Specht, Marcus, Börner, Dirk, Glahn, Christian, Fiedler, Sebastian, Fisichella, Marco, Herder, Eelco, Marenzi, Ivana, Nejdl, Wolfgang, Kawese, Ricardo, Papadakis, George

D1.2 Trends in Connecting Learners. First Research & Technology Scouting Report

2010

In this first STELLAR trend report we survey the more distant future of TEL, as reflected in the roadmaps; we compare the visions with trends in TEL research and TEL practice. This generic overview is complemented by a number of small-scale studies, which focus on a specific technology, approach or pedagogical model.
2010

Lindstaedt Stefanie , Rath Andreas S., Devaurs Didier

Studying the Factors Influencing Automatic User Task Detection on the Computer Desktop

Sustaining TEL: From Innovation to Learning and Practice, Lecture Notes in Computer Science, Springer, 2010

. Supporting learning activities during work has gained momentum fororganizations since work-integrated learning (WIL) has been shown to increaseproductivity of knowledge workers. WIL aims at fostering learning at the workplace,during work, for enhancing task performance. A key challenge for enablingtask-specific, contextualized, personalized learning and work support is to automaticallydetect the user’s task. In this paper we utilize our ontology-based usertask detection approach for studying the factors influencing task detection performance.We describe three laboratory experiments we have performed in twodomains including over 40 users and more than 500 recorded task executions.The insights gained from our evaluation are: (i) the J48 decision tree and Na¨ıveBayes classifiers perform best, (ii) six features can be isolated, which providegood classification accuracy, (iii) knowledge-intensive tasks can be classified aswell as routine tasks and (iv) a classifier trained by experts on standardized taskscan be used to classify users’ personal tasks.
2009

Rath Andreas S., Devaurs Didier, Lindstaedt Stefanie

KnowSe: Fostering User Interaction Context Awareness

Supplementary Proceedings of the 11th European Conference on Computer Supported Cooperative Work (ECSCW '09). Demo Paper, 2009

Konferenz
2009

Granitzer Michael, Rath Andreas S., Kröll Mark, Ipsmiller D., Devaurs Didier, Weber Nicolas, Lindstaedt Stefanie , Seifert C.

Machine Learning based Work Task Classification

Journal of Digital Information Management, 2009

Journal
Increasing the productivity of a knowledgeworker via intelligent applications requires the identification ofa user’s current work task, i.e. the current work context a userresides in. In this work we present and evaluate machine learningbased work task detection methods. By viewing a work taskas sequence of digital interaction patterns of mouse clicks andkey strokes, we present (i) a methodology for recording thoseuser interactions and (ii) an in-depth analysis of supervised classificationmodels for classifying work tasks in two different scenarios:a task centric scenario and a user centric scenario. Weanalyze different supervised classification models, feature typesand feature selection methods on a laboratory as well as a realworld data set. Results show satisfiable accuracy and high useracceptance by using relatively simple types of features.
2009

Rath Andreas S., Devaurs Didier, Lindstaedt Stefanie

Contextualized Knowledge Services for Personalized Learner Support

Fourth European Conference on Technology Enhanced Learning. Demo Paper. Lecture Notes in Computer Science. Springer, 2009

Konferenz
In this demonstration we present our KnowSe framework,developed for observing, storing, analyzing and leveraging ContextualAttention Metadata, utilizing our ontology-based user interactions contextmodel (UICO). It includes highly contextualized knowledge servicesfor supporting learners in a personalized and adaptive way, by exploitingthe learner’s user context.
2009

Rath Andreas S., Devaurs Didier, Lindstaedt Stefanie

Detecting Real User Tasks by Training on Laboratory Contextual Attention Metadata

Proceedings of Exploitation User Attention Metadata (EUAM '09) held at Informatik '09, 2009

Konferenz
Detecting the current task of a user is essential for providing her with contextualizedand personalized support, and using Contextual Attention Metadata (CAM)can help doing so. Some recent approaches propose to perform automatic user task detectionby means of task classifiers using such metadata. In this paper, we show thatgood results can be achieved by training such classifiers offline on CAM gathered inlaboratory settings. We also isolate a combination of metadata features that present asignificantly better discriminative power than classical ones.
2009

Lindstaedt Stefanie , Rath Andreas S., Devaurs Didier

UICO: An Ontology-Based User Interaction Context Model for Automatic Task Detection on the Computer Desktop

Proceedings of the Context Information and Ontology (CIAO2009) workshop as part of the ESWC 2009, Gomez-Perez, J. M., Haase, P., Tilly, M., Warren, P., ACM, 2009

Konferenz
‘Understanding context is vital’ [1] and ‘context is key’ [2]signal the key interest in the context detection field. Oneimportant challenge in this area is automatically detectingthe user’s task because once it is known it is possible tosupport her better. In this paper we propose an ontologybaseduser interaction context model (UICO) that enhancesthe performance of task detection on the user’s computerdesktop. Starting from low-level contextual attention metadatacaptured from the user’s desktop, we utilize rule-based,information extraction and machine learning approaches toautomatically populate this user interaction context model.Furthermore we automatically derive relations between themodel’s entities and automatically detect the user’s task.We present evaluation results of a large-scale user study wecarried out in a knowledge-intensive business environment,which support our approach.
2008

Granitzer Michael, Kröll Mark, Seifer Christin, Rath Andreas S., Weber Nicolas, Dietzel O., Lindstaedt Stefanie

Analysis of Machine Learning Techniques for Context Extraction

Proceedings of 2008 International Conference on Digital Information Management (ICDIM08), IEEE Computer Society Press, 2008

Konferenz
’Context is key’ conveys the importance of capturing thedigital environment of a knowledge worker. Knowing theuser’s context offers various possibilities for support, likefor example enhancing information delivery or providingwork guidance. Hence, user interactions have to be aggregatedand mapped to predefined task categories. Withoutmachine learning tools, such an assignment has to be donemanually. The identification of suitable machine learningalgorithms is necessary in order to ensure accurate andtimely classification of the user’s context without inducingadditional workload.This paper provides a methodology for recording user interactionsand an analysis of supervised classification models,feature types and feature selection for automatically detectingthe current task and context of a user. Our analysisis based on a real world data set and shows the applicabilityof machine learning techniques.
2008

Granitzer Michael, Granitzer Gisela, Lindstaedt Stefanie , Rath Andreas S., Groiss W.

Automating Knowledge Transfer and Creation in Knowledge Intensive Business Processes

Proceedings of the First Workshop on Business Process Management and Social Software BPMS2 08, September 1, 2008, Mailand, Italien, Springer, 2008

It is a well known fact that a wealth of knowledge lies in thehead of employees making them one of the most or even the most valuableasset of organisations. But often this knowledge is not documented andorganised in knowledge systems as required by the organisation, butinformally shared. Of course this is against the organisation’s aim forkeeping knowledge reusable as well as easily and permanently availableindependent of individual knowledge workers.In this contribution we suggest a solution which captures the collectiveknowledge to the benefit of the organisation and the knowledge worker.By automatically identifying activity patterns and aggregating them totasks as well as by assigning resources to these tasks, our proposed solutionfulfils the organisation’s need for documentation and structuring ofknowledge work. On the other hand it fulfils the the knowledge worker’sneed for relevant, currently needed knowledge, by automatically miningthe entire corporate knowledge base and providing relevant, contextdependent information based on his/her current task.
2008

Rath Andreas S., Weber Nicolas, Kröll Mark, Granitzer Michael, Dietzel O., Lindstaedt Stefanie

Context-Aware Knowledge Services

Workshop on Personal Information Management (PIM2008) at the 26th Computer Human Interaction Conference (CHI2008), Florence, Italy, 2008

Konferenz
Improving the productivity of knowledge workers is anopen research challenge. Our approach is based onproviding a large variety of knowledge services which takethe current work task and information need (work context)of the knowledge worker into account. In the following wepresent the DYONIPOS application which strives toautomatically identify a user’s work task and thencontextualizes different types of knowledge servicesaccordingly. These knowledge services then provideinformation (documents, people, locations) both from theuser’s personal as well as from the organizationalenvironment. The utility and functionality is illustratedalong a real world application scenario at the Ministry ofFinance in Austria.
2007

Kröll Mark, Rath Andreas S., Weber Nicolas, Lindstaedt Stefanie , Granitzer Michael

Task Instance Classification via Graph Kernels

Mining and Learning with Graphs (MLG 07), Florenz, Italy, August 1-3, 2007, 2007

Journal
2007

Rath Andreas S., Kröll Mark, Lindstaedt Stefanie , Granitzer Michael

Low-Level Event Relationship Discovery for Knowledge Work Support

Proccedings of the 4th Conference on Professional Knowledge Management WM2007, ProKW2007, 28. - 30. März 2007, Potsdam, Germany, Gronau, N., GITO-Verlag, Berlin, 2007

Konferenz
2007

Rath Andreas S.

A Low-Level Based Task and Process Support Approach For Knowledge-Intensive Business Environments

In Proceedings of the 5th International Conference on Enterprise Information System Doctoral Consortium (DCEIS 2007), Funchal, Portugal, Funchal, 2007

Konferenz
2006

Rath Andreas S., Kröll Mark, Andrews K., Lindstaedt Stefanie , Granitzer Michael

Synergizing Standard and Ad-Hoc Processes

Lecture Notes in Computer Science LNAI 4333, International Conference on Practical Aspects of Knowledge Management, Springer Berlin, Berlin Heidelberg, 2006

Konferenz
In a knowledge-intensive business environment, knowledgeworkers perform their tasks in highly creative ways. This essential freedomrequired by knowledge workers often conflicts with their organization’sneed for standardization, control, and transparency. Within thiscontext, the research project DYONIPOS aims to mitigate this contradictionby supporting the process engineer with insights into the processexecuter’s working behavior. These insights constitute the basis for balancedprocess modeling. DYONIPOS provides a process engineer supportenvironment with advanced process modeling services, such as processvisualization, standard process validation, and ad-hoc process analysisand optimization services.
2006

Granitzer Michael, Lindstaedt Stefanie , Tochtermann K., Kröll Mark, Rath Andreas S.

Contextual Retrieval in Knowledge Intensive Business Environments

Proceedings LWA 2006 - Lernen - Wissensentdeckung - Adaptivität, Hildesheim, Germany, October 9-11, 2006, Schaaf, M., Althoff, D., Universität Hildesheim, Hildesheim, 2006

Konferenz
Knowledge-intensive work plays an increasinglyimportant role in organisations of all types. Thiswork is characterized by a defined input and adefined output but not the way how to transformthe input to an output. Within this context, theresearch project DYONIPOS aims at encouragingthe two crucial roles in a knowledge-intensiveorganization - the process executer and the processengineer. Ad-hoc support will be providedfor the knowledge worker by synergizing the developmentof context sensitive, intelligent, andagile semantic technologies with contextual retrieval.DYONIPOS provides process executerswith guidance through business processes andjust-in-time resource support based on the currentuser context, that are the focus of this paper.
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