Trajectory Similarity Join for Spatial Temporal Database

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Abstract

The trajectory similarity join aims to find similar trajectory pairs from two large collections of trajectories. This join targets applications such as trajectory near-duplicate detection, ridesharing recommendation and so on. Extensive works have been conducted on addressing this join. However, most of them only focus on spatial dimension without combining temporal range together. To address problem, this paper proposes a novel two-level grid index which takes both spatial and temporal range into account when processing spatial-temporal similarity join, and signature based dynamic grid warping (SDGW) approach to evaluate the spatial similarity for trajectory pairs. Some pruning approaches are developed to improve the query processing. In addition, extensive experiments are conducted to verify the efficiency and scalability of our methods.

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Dan, T., Luo, C., Li, Y., & Zhang, C. (2019). Trajectory Similarity Join for Spatial Temporal Database. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11707 LNCS, pp. 306–321). Springer. https://doi.org/10.1007/978-3-030-27618-8_23

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