Scalable Spatio-Temporal Continuous Query Processing for Location-Aware Services

Provided by: Purdue Federal Credit Union
Topic: Data Management
Format: PDF
Real-time spatio-temporal query processing needs to effectively handle a large number of moving objects and continuous spatio-temporal queries. In this paper, the authors use shared execution as a mechanism to support scalability in location-aware servers. Their main idea is to maintain a query table that stores information about continuous spatio-temporal queries. Then, answering spatio-temporal queries is abstracted as a spatial join among the moving objects and queries. Three query join policies are proposed aiming to minimize the cost of the join operation under the shared execution paradigm, namely the Clock-triggered Join Policy, the Incremental Join Policy, and the Hot Join Policy. They introduce the concept of a No-Action Region that is used in conjunction with the hot join policy.

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