Scalable Spatio-temporal Continuous Query Processing for Location-aware Services

  • Authors:

  • Xiaopeng Xiong;Mohamed F. Mokbel;Walid G. Aref;Susanne E. Hambrusch;Sunil Prabhakar

  • Affiliations:

  • Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN

  • Venue:
  • SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
  • Year:
  • 2004

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Abstract

Real-time spatio-temporal query processing needs toeffectively handle a large number of moving objectsand continuous spatio-temporal queries. In this paper,we use shared execution as a mechanism to supportscalability in location-aware servers. Our main ideais to maintain a query table that stores informationabout continuous spatio-temporal queries. Then, answering spatio-temporal queries is abstracted as a spatial join among the moving objects and queries. Threequery join policies are proposed aiming to minimize thecost of the join operation under the shared executionparadigm, namely the Clock-triggered Join Policy, theIncremental Join Policy, and the Hot Join Policy. Weintroduce the concept of a No-Action Region that isused in conjunction with the hot join policy. We propose algorithms that calculate the No-Action region for objects and queries. Experimental performance demonstrates that the No-Action region is more efficient thanother approaches when used along with the hot join policy. Experiments also demonstrate that the hot join policy outperforms the clock-triggered join policy andthe incremental join policy in terms of both I/O andCPU costs.