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Top-k Pattern Matching Using an Information-Theoretic Criterion over Probabilistic Data Streams

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Book cover Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10366))

Abstract

As the development of data mining technologies for sensor data streams, more sophisticated methods for complex event processing are demanded. In the case of event recognition, since event recognition results may contain errors, we need to deal with the uncertainty of events. We therefore consider probabilistic event data streams with occurrence probabilities of events, and develop a pattern matching method based on regular expressions. In this paper, we first analyze the semantics of pattern matching over non-probabilistic data streams, and then propose the problem of top-k pattern matching over probabilistic data streams. We introduce the use of an information-theoretic criterion to select appropriate matches as the result of pattern matching. Then, we present an efficient algorithm to detect top-k matches, and evaluate the effectiveness of our approach using real and synthetic datasets.

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Acknowledgment

This research was partially supported by the Center of Innovation Program from Japan Science and Technology Agency (JST) and KAKENHI (16H01722, 26540043).

Author information

Authors and Affiliations

  1. Graduate School of Information Science, Nagoya University, Nagoya, Japan

    Kento Sugiura

  2. Graduate School of Informatics, Nagoya University, Nagoya, Japan

    Yoshiharu Ishikawa

Authors
  1. Kento Sugiura
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  2. Yoshiharu Ishikawa
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Corresponding author

Correspondence to Kento Sugiura .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

  2. Computer Science, Aarhus University, Aarhus N, Denmark

    Christian S. Jensen

  3. Computer Science, University of Southern California, Los Angeles, California, USA

    Cyrus Shahabi

  4. Northeastern University, Shenyang, China

    Xiaochun Yang

  5. Kent State University, Kent, Ohio, USA

    Xiang Lian

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Sugiura, K., Ishikawa, Y. (2017). Top-k Pattern Matching Using an Information-Theoretic Criterion over Probabilistic Data Streams. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_39

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  • DOI: https://doi.org/10.1007/978-3-319-63579-8_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63578-1

  • Online ISBN: 978-3-319-63579-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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