A correlation-aware partial materialization scheme for near real-time automotive queries

Abstract

Real-time aggregate queries can help obtain interested summary of traffic information on the road. However, due to unreliable connection and limited duration in Vehicular Ad hoc Networks (VANETs), it is difficult to carry out the online computation over all received traffic messages. In order to improve query accuracy and provide quick query response, we propose a novel scheme for real-time aggregate queries, called Road Cube, which essentially makes use of precomputation on interested traffic messages. We utilize Information Retrieval (IR) technique to identify interested information that potentially shows semantic correlation and can be indexed in future with high probability. The Road Cube improves upon conventional data cube by exploiting semantic correlation of multi-dimensional attributes existing in received traffic information so as to obtain partial materialization. The partial materialization usually satisfies real-time and space requirements in VANETs. Extensive performance evaluation based on real-world map and traffic models shows that the Road Cube obtains significant performance improvements, compared with the conventional approaches.