Pruning Optimization over Threshold-Based Historical Continuous Query
AbstractWith 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. View Full-Text
Share & Cite This Article
Qin, J.; Ma, L.; Liu, Q. Pruning Optimization over Threshold-Based Historical Continuous Query. Algorithms 2019, 12, 107.
Qin J, Ma L, Liu Q. Pruning Optimization over Threshold-Based Historical Continuous Query. Algorithms. 2019; 12(5):107.Chicago/Turabian Style
Qin, Jiwei; Ma, Liangli; Liu, Qing. 2019. "Pruning Optimization over Threshold-Based Historical Continuous Query." Algorithms 12, no. 5: 107.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.