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Trajectory Data Pattern Mining

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Book cover New Frontiers in Mining Complex Patterns (NFMCP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8399))

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Abstract

In this paper, we study the problem of mining for frequent trajectories, which is crucial in many application scenarios, such as vehicle traffic management, hand-off in cellular networks, supply chain management. We approach this problem as that of mining for frequent sequential patterns. Our approach consists of a partitioning strategy for incoming streams of trajectories in order to reduce the trajectory size and represent trajectories as strings. We mine frequent trajectories using a sliding windows approach combined with a counting algorithm that allows us to promptly update the frequency of patterns. In order to make counting really efficient, we represent frequent trajectories by prime numbers, whereby the Chinese reminder theorem can then be used to expedite the computation.

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Notes

  1. 1.

    The % is the classical modulo operation that computes the remainder of the division.

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Author information

Authors and Affiliations

  1. ICAR-CNR, Naples, Italy

    Elio Masciari

  2. UCLA, Los Angeles, USA

    Gao Shi & Carlo Zaniolo

Authors
  1. Elio Masciari
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  2. Gao Shi
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  3. Carlo Zaniolo
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Corresponding author

Correspondence to Elio Masciari .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  3. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  4. ICAR, CNR, Rende, Italy

    Giuseppe Manco

  5. Rende, Italy

    Elio Masciari

  6. Department of Computer Science, University of North Carolina, Charlotte, North Carolina, USA

    Zbigniew W. Ras

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© 2014 Springer International Publishing Switzerland

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Masciari, E., Shi, G., Zaniolo, C. (2014). Trajectory Data Pattern Mining. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2013. Lecture Notes in Computer Science(), vol 8399. Springer, Cham. https://doi.org/10.1007/978-3-319-08407-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-08407-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08406-0

  • Online ISBN: 978-3-319-08407-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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