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PSMSP: A Parallelized Sampling-Based Approach for Mining Top-k Sequential Patterns in Database Graphs

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Database Systems for Advanced Applications (DASFAA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11448))

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Abstract

We study to improve the efficiency of finding top-k sequential patterns in database graphs, where each edge (or vertex) is associated with multiple transactions and a transaction consists of a set of items. This task is to discover the subsequences of transaction sequences that frequently appear in many paths. We propose PSMSP, a Parallelized Sampling-based Approach For Mining Top-k Sequential Patterns, which involves: (a) a parallelized unbiased sequence sampling approach, and (b) a novel PSP-Tree structure to efficiently mine the patterns based on the anti-monotonicity properties. We validate our approach via extensive experiments with real-world datasets.

This work has been supported in part by the National Key Research and Development Program of China (No. 2017YFB0803301), the Natural Science Foundation of China (No. U1836215), and DongGuan Innovative Research Team Program (No. 201636000100038).

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References

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Author information

Authors and Affiliations

  1. Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China

    Mingtao Lei, Xi Zhang, Jincui Yang & Binxing Fang

  2. Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, China

    Binxing Fang

Authors
  1. Mingtao Lei
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  2. Xi Zhang
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  3. Jincui Yang
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  4. Binxing Fang
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Corresponding author

Correspondence to Xi Zhang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Guoliang Li

  2. Duke University, Durham, NC, USA

    Jun Yang

  3. University of Porto, Porto, Portugal

    Joao Gama

  4. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

  5. Beihang University, Beijing, China

    Yongxin Tong

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Lei, M., Zhang, X., Yang, J., Fang, B. (2019). PSMSP: A Parallelized Sampling-Based Approach for Mining Top-k Sequential Patterns in Database Graphs. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_23

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  • DOI: https://doi.org/10.1007/978-3-030-18590-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18589-3

  • Online ISBN: 978-3-030-18590-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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