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An Overview of Moving Object Trajectory Compression Algorithms

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  • Penghui Sun
  • Shixiong Xia
  • Guan Yuan
  • Daxing Li

Abstract

Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential increase in the amount of such trajectory data has caused a series of problems in the storage, processing, and analysis of data. Therefore, moving object trajectory compression undoubtedly becomes one of the hotspots in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object compression and analyze typical moving object compression algorithms presented in recent years. In this paper, we firstly summarize the strategies and implementation processes of classical moving object compression algorithms. Secondly, the related definitions about moving objects and their trajectories are discussed. Thirdly, the validation criteria are introduced for evaluating the performance and efficiency of compression algorithms. Finally, some application scenarios are also summarized to point out the potential application in the future. It is hoped that this research will serve as the steppingstone for those interested in advancing moving objects mining.

Suggested Citation

  • Penghui Sun & Shixiong Xia & Guan Yuan & Daxing Li, 2016. "An Overview of Moving Object Trajectory Compression Algorithms," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-13, May.
  • Handle: RePEc:hin:jnlmpe:6587309
    DOI: 10.1155/2016/6587309
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    Cited by:

    1. Zhongqiu Wang & Guan Yuan & Haoran Pei & Yanmei Zhang & Xiao Liu, 2020. "Unsupervised learning trajectory anomaly detection algorithm based on deep representation," International Journal of Distributed Sensor Networks, , vol. 16(12), pages 15501477209, December.
    2. Daniel Amigo & David Sánchez Pedroche & Jesús García & José Manuel Molina, 2021. "Review and classification of trajectory summarisation algorithms: From compression to segmentation," International Journal of Distributed Sensor Networks, , vol. 17(10), pages 15501477211, October.
    3. Guan Yuan & Zhongqiu Wang & Zhixiao Wang & Fukai Zhang & Li Yuan & Jian Zhang, 2019. "APDS: A framework for discovering movement pattern from trajectory database," International Journal of Distributed Sensor Networks, , vol. 15(11), pages 15501477198, November.

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