Matching Consecutive Subpatterns over Streaming Time Series

  • Rong Kang
  • Chen Wang
  • Peng Wang
  • Yuting Ding
  • Jianmin WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10988)


Pattern matching of streaming time series with lower latency under limited computing resource comes to a critical problem, especially as the growth of Industry 4.0 and Industry Internet of Things. However, against traditional single pattern matching model, a pattern may contain multiple subpatterns representing different physical meanings in the real world. Hence, we formulate a new problem, called “consecutive subpatterns matching”, which allows users to specify a pattern containing several consecutive subpatterns with various specified thresholds. We propose a novel representation Equal-Length Block (ELB) together with two efficient implementations, which work very well under all \(L_p\)-Norms without false dismissals. Extensive experiments are performed on synthetic and real-world datasets to illustrate that our approach outperforms the brute-force method and MSM, a multi-step filter mechanism over the multi-scaled representation by orders of magnitude.


Pattern matching Stream Time series 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rong Kang
    • 1
    • 2
  • Chen Wang
    • 1
    • 2
  • Peng Wang
    • 3
  • Yuting Ding
    • 1
    • 2
  • Jianmin Wang
    • 1
    • 2
    Email author
  1. 1.School of SoftwareTsinghua UniversityBeijingChina
  2. 2.National Engineering Laboratory for Big Data SoftwareTsinghua UniversityBeijingChina
  3. 3.School of Computer ScienceFudan UniversityShanghaiChina

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