Application Based Distance Measurement for Context Retrieval in Ubiquitous Computing

Building large-scale smart environments is one of the long-term goals of ubiquitous computing. The widespread of context information in such environments necessitates an effective context retrieval mechanism. This paper proposes a novel context retrieval method based on applications´ query patterns. We propose high dimensional vector to model contexts from applications´ perspective, and apply the normalized inner product of high dimensional vectors to measure context distance. Contexts with similar query patterns are clustered into the same group. To improve the performance of context retrieval, we build distributed indices on each node to speed up a local search, and create shortcuts based on clustering results to facilitate query routing. We show how our proposed methods can be applied to existing context retrieval mechanisms. Our experimental results show that our method can significantly reduce retrieval cost.