Hybrid reasoning technique for improving context-aware applications

With the rapid adoption of GPS enabled smart phones and the fact that users are almost permanently connected to the Internet, an evolution is observed toward applications and services that adapt themselves using the user's context, a.o. taking into account location information. To facilitate the development of such new intelligent applications, new enabling platforms are needed to collect, distribute, and exchange context information. An important aspect of such platforms is the derivation of new, high-level knowledge by combining different types of context information using reasoning techniques. In this paper, we present a new approach to derive context information by combining case-based and rule-based reasoning. Two use cases are detailed where both reasoners are used to derive extra useful information. For the desk sharing office use case, the combination of rule-based and case-based reasoning allows to automatically learn typical trajectories of a user and improve localization on such trajects with 42%. In both use cases, the hybrid approach is shown to provide a significant improvement