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International Conference on Database Systems for Advanced Applications

DASFAA 2019: Database Systems for Advanced Applications pp 124–139Cite as

Efficient Mining of Event Periodicity in Data Series

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11446))

Abstract

This paper investigates the problem of efficiently discovering periodicity of a certain event in data series. To that end, the current work argues firstly that the periodicity of an event in data series may be formalized as the distribution period, the structure period, or the both. Along this line, a partition method, \(\pi (n)\), is proposed to divide the data series into length-equal and position-continuous segments. Based on the results of implementing \(\pi (n)\) on a data series, we propose two new concepts of distribution periodicity and structure periodicity. Then, a cross-entropy-based method, namely CEPD, is proposed to mine the periodicity of data series. The experimental results show that CEPD can be used to mine feasible event periodicity in data series, especially, with very low level of time consumption and high capability of noise resilience.

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Notes

  1. 1.

    \(|P_{\lceil \frac{|S|}{n}\rceil }|\le n\) is allowed.

  2. 2.

    \(supp(x|P_{\lceil \frac{|S|}{n}\rceil })+supp(\tilde{x}|P_{\lceil \frac{|S|}{n}\rceil })\) may less than n while incomplete partition happened in the last segment.

  3. 3.

    In Fig. 1, symbol \(^\dagger \) means the experimental results without WARP.

  4. 4.

    http://archive.ics.uci.edu/ml/datasets/.

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Acknowledgments

The authors would like to thank the supports of the National Natural Science Foundation of China (71671027/91846105/71572029/71490723).

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Authors and Affiliations

  1. School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Hua Yuan, Yu Qian & Mengna Bai

Authors
  1. Hua Yuan
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  2. Yu Qian
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  3. Mengna Bai
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Corresponding author

Correspondence to Hua Yuan .

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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Yuan, H., Qian, Y., Bai, M. (2019). Efficient Mining of Event Periodicity in Data Series. 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 11446. Springer, Cham. https://doi.org/10.1007/978-3-030-18576-3_8

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  • DOI: https://doi.org/10.1007/978-3-030-18576-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18575-6

  • Online ISBN: 978-3-030-18576-3

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