Judie Attard
ABSTRACT
Today's personal devices provide a stream of information which, if processed adequately, can provide a better insight into their owner's current activities, environment, location, etc. In treating these devices as part of a personal sensor network, we exploit raw and interpreted context information in order to enable the automatic recognition of personal recurring situations. An ontology-based graph matching technique continuously compares a person's 'live context', with all previously-stored situations, both of which are represented as an instance of the DCON Context Ontology. Whereas each situation corresponds to an adaptive DCON instance, initially marked by a person and gradually characterised over time, the live context representation is constantly updated with mashed-up context information streaming in from various personal sensors. In this paper we present the matching technique employed to enable automatic situation recognition, and an experiment to evaluate its performance based on real users and their perceived context data.
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