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Detect Tracking Behavior Among Trajectory Data

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Book cover Advanced Data Mining and Applications (ADMA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10604))

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Abstract

Due to the continuing improvements in location acquisition technology, a large population of GPS-equipped moving objects are tracked in a server. In emergency applications, users may want to detect whether a target is tracked by another object. We formulate the tracking behavior by continuous distance queries in trajectory databases. Index structures are developed to improve the query performance. Using real trajectories, we demonstrate answering continuous distance queries in a database system and animating moving objects fulfilling the distance condition in the user interface. The result benefits mining the interesting behavior among trajectory data and answering distance join queries.

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Acknowledgment

This work is supported by NSFC under grant numbers 61300052 and the Fundamental Research Funds for the Central Universities NO. NZ2013306.

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Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Jianqiu Xu & Jiangang Zhou

Authors
  1. Jianqiu Xu
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  2. Jiangang Zhou
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Corresponding author

Correspondence to Jianqiu Xu .

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Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Gao Cong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  3. Macquarie University, Sydney, New South Wales, Australia

    Wei Emma Zhang

  4. Wuhan University, Wuhan, China

    Chengliang Li

  5. Nanyang Technological University, Singapore, Singapore

    Aixin Sun

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Xu, J., Zhou, J. (2017). Detect Tracking Behavior Among Trajectory Data. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_64

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  • DOI: https://doi.org/10.1007/978-3-319-69179-4_64

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69178-7

  • Online ISBN: 978-3-319-69179-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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