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A Parallelized Frequent Temporal Pattern Mining Algorithm on a Time Series Database

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Intelligent Information and Database Systems (ACIIDS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12672))

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Abstract

For many years, time series data have been considered as one of the most popular significant data forms in our daily life. Time series exist in many application domains such as finance, medicine, geology, meteorology, and telecommunication. Among the time series mining tasks, frequent temporal pattern discovery is an interesting task because this task brings us a deep insight view of relationships between many objects and events through time. However, it is challenging when a combinatorial explosion occurs with many longer time series. It is more challenging if more informative patterns are required from a time series database. In this paper, we propose a parallel algorithm, called PTP, to cope with the frequent temporal pattern mining task. Our PTP is developed with multithreading on a frequent temporal pattern tree where each branch is processed in parallel. In addition, our PTP maintains the details of each frequent temporal pattern not only from its frequent occurrences in an individual time series but also from its frequent inter-time series associations. As a result, frequent temporal patterns discovered by PTP are more informative with explicit and exact temporal information, showing the relationships among the objects/events corresponding to the time series. Through the experimental results on real time series, PTP outperforms the brute force algorithm and the existing non-parallel algorithm in terms of both time and space. The found frequent temporal patterns can be further analyzed for other tasks such as prediction, classification, and clustering.

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Authors and Affiliations

  1. HCMC University of Foreign Language and Information Technology, Ho Chi Minh City, Vietnam

    Nguyen Thanh Vu

  2. Ho Chi Minh City University of Technology, Vietnam National University–HCMC, Ho Chi Minh City, Vietnam

    Chau Vo

Authors
  1. Nguyen Thanh Vu
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  2. Chau Vo
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Correspondence to Nguyen Thanh Vu or Chau Vo .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

  3. Nanyang Technological University, Singapore, Singapore

    Dusit Niyato

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Vu, N.T., Vo, C. (2021). A Parallelized Frequent Temporal Pattern Mining Algorithm on a Time Series Database. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_7

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  • DOI: https://doi.org/10.1007/978-3-030-73280-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73279-0

  • Online ISBN: 978-3-030-73280-6

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