Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen


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.

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

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.

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

’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.
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