Scalable spatio-temporal continuous query processing for location-aware services

Real-time spatio-temporal query processing needs to effectively handle a large number of moving objects and continuous spatio-temporal queries. In this paper, we use shared execution as a mechanism to support scalability in location-aware servers. Our 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. We introduce the concept of a no-action region that is used 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 than other approaches when used along with the hot join policy. Experiments also demonstrate that the hot join policy outperforms the clock-triggered join policy and the incremental join policy in terms of both I/O and CPU costs.