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Trends and Applications in Knowledge Discovery and Data Mining pp 41–52Cite as

Mining Massive-Scale Spatiotemporal Trajectories in Parallel: A Survey

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9441))

Abstract

With the popularization of positioning devices such as GPS navigators and smart phones, large volumes of spatiotemporal trajectory data have been produced at unprecedented speed. For many trajectory mining problems, a number of computationally efficient approaches have been proposed. However, to more effectively tackle the challenge of big data, it is important to exploit various advanced parallel computing paradigms. In this paper, we present a comprehensive survey of the state-of-the-art techniques for mining massive-scale spatiotemporal trajectory data based on parallel computing platforms such as Graphics Processing Unit (GPU), MapReduce and Field Programmable Gate Array (FPGA). This survey covers essential topics including trajectory indexing and query, clustering, join, classification, pattern mining and applications. We also give an in-depth analysis of the related techniques and compare them according to their principles and performance.

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Author information

Authors and Affiliations

  1. Intelligent Computing Lab, Division of Informatics, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, People’s Republic of China

    Pengtao Huang & Bo Yuan

Authors
  1. Pengtao Huang
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  2. Bo Yuan
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Correspondence to Pengtao Huang .

Editor information

Editors and Affiliations

  1. Institute of Infocomm Research, Singapore, Singapore

    Xiao-Li Li

  2. Ho Chi Minh City University of Tech, Ho Chi Minh City, Vietnam

    Tru Cao

  3. School of Information Systems, Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Science & Technology, Japan Advanced Institute of, Nomi-shi, Ishikawa, Japan

    Tu-Bao Ho

  6. The University of Hong Kong, Hong Kong, China

    David Cheung

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Huang, P., Yuan, B. (2015). Mining Massive-Scale Spatiotemporal Trajectories in Parallel: A Survey. In: Li, XL., Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D. (eds) Trends and Applications in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science(), vol 9441. Springer, Cham. https://doi.org/10.1007/978-3-319-25660-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-25660-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25659-7

  • Online ISBN: 978-3-319-25660-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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