Abstract
Existing works only focus on spatial dimension without the consideration of combining spatial and temporal dimensions together when processing trajectory similarity join queries, to address this problem, this paper proposes a novel two-level grid index which takes both spatial and temporal information into account when processing spatial-temporal trajectory similarity join. A new similarity function MOGS is developed to measure the similarity in an efficient manner when our candidate trajectories have high coverage rate CR. Extensive experiments are conducted to verify the efficiency of our solution.
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Acknowledgments
This work is supported in part by Hubei Natural Science Foundation under Grant No. 2017CFB135, NSFC Grant No. 61309002, and the Fundamental Research Funds for the Central Universities under Grants No. CCNU18QN017, CZZ17003, and Teaching Research Projects NO. JYX17032.
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Dan, T., Luo, C., Li, Y., Zheng, B., Li, G. (2019). Spatial Temporal Trajectory Similarity Join. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11642. Springer, Cham. https://doi.org/10.1007/978-3-030-26075-0_19
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DOI: https://doi.org/10.1007/978-3-030-26075-0_19
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