Automatic description of context-altering services through observational learning

Authors:
Katharina Rasch;Fei Li;Sanjin Sehic;Rassul Ayani;Schahram Dustdar
Affiliations:
School of Information and Communication Technology, KTH Royal Institute of Technology, Stockholm, Sweden;Distributed Systems Group, Vienna University of Technology, Wien, Austria;Distributed Systems Group, Vienna University of Technology, Wien, Austria;School of Information and Communication Technology, KTH Royal Institute of Technology, Stockholm, Sweden;Distributed Systems Group, Vienna University of Technology, Wien, Austria
Venue:
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Year:
2012

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Abstract

Understanding the effect of pervasive services on user context is critical to many context-aware applications. Detailed descriptions of context-altering services are necessary, and manually adapting them to the local environment is a tedious and error-prone process. We present a method for automatically providing service descriptions by observing and learning from the behavior of a service with respect to its environment. By applying machine learning techniques on the observed behavior, our algorithms produce high quality localized service descriptions. In a series of experiments we show that our approach, which can be easily plugged into existing architectures, facilitates context-awareness without the need for manually added service descriptions.