MQuery: Fast Graph Query via Semantic Indexing for Mobile Context

Abstract

Mobile is becoming a ubiquitous platform for context-aware intelligent computing. One fundamental but usually ignored issue is how to efficiently manage (e.g., index and query) the mobile context data. To this end, we present a unified framework and have developed a toolkit, referred to as MQuery. More specifically, the mobile context data is represented in the standard RDF (Resource Description Framework) format. We propose a compressed-index method which takes less than 50% of the memory cost (of the traditional method) to index the context data. Four query interfaces have been developed for efficiently querying the context data including: instance query, neighbor query, shortest path query, and connection subgraph query. Experimental results on two real datasets demonstrate the efficiency of MQuery.