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International Conference on Algorithms and Architectures for Parallel Processing

ICA3PP 2020: Algorithms and Architectures for Parallel Processing pp 218–230Cite as

Detection of Loose Tracking Behavior over Trajectory Data

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12454))

Abstract

The improvements in location-acquisition technologies make massive trajectory data available. Discovering objects that move together over trajectory data is beneficial in many applications. In this paper, a novel concept called loose tracking behavior is proposed to investigate the problem of detecting objects that travel together with a target. We develop two algorithms to solve the problem. The first one is a straightforward approach and the second one is an improved algorithm where we develop a prefix tree index structure for the trajectories encoded by Geohash to enhance the detection efficiency. Finally, the effectiveness of the proposed concepts and the efficiency of the approaches are validated by extensive experiments based on real trajectory datasets.

Supported by the National Natural Science Foundation of China under the grant Nos. 61872197, 61972209 and 61872193; the Postdoctoral Science Foundation of China under the Grand No. 2019M651919; the Natural Research Foundation of NJUPT under the grand No. NY217119.

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Author information

Authors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

    Jiawei Li, Hua Dai, Yiyang Liu & Geng Yang

  2. Jiangsu Security and Intelligent Processing Lab of Big Data, Nanjing, 210023, China

    Hua Dai & Geng Yang

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, 211016, China

    Jianqiu Xu

  4. Jiangsu Sealevel Data Technology Co., Ltd., Nanjing, 210016, China

    Jie Sun

Authors
  1. Jiawei Li
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  2. Hua Dai
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  3. Yiyang Liu
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  4. Jianqiu Xu
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  5. Jie Sun
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  6. Geng Yang
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Corresponding author

Correspondence to Hua Dai .

Editor information

Editors and Affiliations

  1. Columbia University, New York, NY, USA

    Meikang Qiu

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Li, J., Dai, H., Liu, Y., Xu, J., Sun, J., Yang, G. (2020). Detection of Loose Tracking Behavior over Trajectory Data. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12454. Springer, Cham. https://doi.org/10.1007/978-3-030-60248-2_15

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