Segmental Semi-Markov Model Based Online Series Pattern Detection Under Arbitrary Time Scaling

  • Guangjie Ling
  • Yuntao Qian
  • Sen Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


Efficient online detection of similar patterns under arbitrary time scaling of a given time sequence is a challenging problem in the real-time application field of time series data mining. Some methods based on sliding window have been proposed. Although their ideas are simple and easy to realize, their computational loads are very expensive. Therefore, model based methods are proposed. Recently, the segmental semi-Markov model is introduced into the field of online series pattern detection. However, it can only detect the matching sequences with approximately equal length to that of the query pattern. In this paper, an improved segmental semi-Markov model, which can solve this challenging problem, is proposed. And it is successfully demonstrated on real data sets.


Hide Markov Model Dynamic Time Warping Pattern Detection Query Pattern Motion Capture Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guangjie Ling
    • 1
    • 2
  • Yuntao Qian
    • 1
    • 2
  • Sen Jia
    • 1
    • 2
  1. 1.College of Computer ScienceZhejiang UniversityHangzhouP.R. China
  2. 2.State key Laboratory of Information SecurityInstitute of Software of Chinese Academy of SciencesBeijingP.R. China

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