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Trajectory Similarity Join for Spatial Temporal Database

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Book cover Database and Expert Systems Applications (DEXA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11707))

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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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Notes

  1. 1.

    https://www.uber.com/.

  2. 2.

    https://lab-work.github.io/data/.

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Acknowledgments

This work is supported in part by Hubei Natural Science Foundation under Grant No. 2017CFB135, and the Fundamental Research Funds for the Central Universities under Grants No. CCNU18QN017, CZZ17003, and Teaching Research Projects NO. JYX17032, and NSFC Grant No. 61309002.

Author information

Authors and Affiliations

  1. School of Computer, Central China Normal University, Wuhan, China

    Tangpeng Dan, Changyin Luo & Chenyuan Zhang

  2. Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, China

    Tangpeng Dan & Changyin Luo

  3. National Language Resources Monitor and Research Center for Network Media, Central China Normal University, Wuhan, China

    Tangpeng Dan & Changyin Luo

  4. College of Computer Science, South-Central University for Nationalities, Wuhan, China

    Yanhong Li

Authors
  1. Tangpeng Dan
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  2. Changyin Luo
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  3. Yanhong Li
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  4. Chenyuan Zhang
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Corresponding author

Correspondence to Changyin Luo .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. The University of Texas at Arlington, Arlington, TX, USA

    Sharma Chakravarthy

  4. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Anderst-Kotsis

  5. Software Competence Center Hagenberg, Hagenberg im Mühlkreis, Austria

    A Min Tjoa

  6. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Dan, T., Luo, C., Li, Y., Zhang, C. (2019). Trajectory Similarity Join for Spatial Temporal Database. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11707. Springer, Cham. https://doi.org/10.1007/978-3-030-27618-8_23

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  • DOI: https://doi.org/10.1007/978-3-030-27618-8_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27617-1

  • Online ISBN: 978-3-030-27618-8

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