Publikationen

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

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

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.

Link

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.

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.

Link

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.

Link

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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