On the stability of context prediction

Authors:
Immanuel König;Bernd Niklas Klein;Klaus David
Affiliations:
University of Kassel, Kassel, Germany;IdE Institute decentralised Energy Technology, Kassel, Germany;University of Kassel, Kassel, Germany
Venue:
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Year:
2013

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Abstract

Context prediction is a key technique for proactive environments adapting to user's needs. To prevent wrong predictions is one key factor to achieve a high user acceptance. A wrong prediction could be caused by faulty or disturbed sensor data. With the triumph of the Smartphone, a wide range of context sources has become ubiquitous. Often, context prediction approaches today do not utilize these multiple context sources to cope with faulty or disturbed sensor data. We propose and evaluate an approach that uses multiple context sources and exploits the correlations between context sources of one user to get a more fault tolerant prediction.