Pruning Optimization over Threshold-Based Historical Continuous Query

Author:

Qin Jiwei,Ma Liangli,Liu Qing

Abstract

With the increase in mobile location service applications, spatiotemporal queries over the trajectory data of moving objects have become a research hotspot, and continuous query is one of the key types of various spatiotemporal queries. In this paper, we study the sub-domain of the continuous query of moving objects, namely the pruning optimization over historical continuous query based on threshold. Firstly, for the problem that the processing cost of the Mindist-based pruning strategy is too large, a pruning strategy based on extended Minimum Bounding Rectangle overlap is proposed to optimize the processing overhead. Secondly, a best-first traversal algorithm based on E3DR-tree is proposed to ensure that an accurate pruning candidate set can be obtained with accessing as few index nodes as possible. Finally, experiments on real data sets prove that our method significantly outperforms other similar methods.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science

Reference19 articles.

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