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Micro Similarity Queries in Time Series Database

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Advances in Knowledge Discovery and Data Mining (PAKDD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2035))

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Abstract

Currently there is no model available that would facilitate the task of finding similar time series based on partial information that interest users. We studied a novel query problem class that we termed micro similarity queries (MSQ) in this paper. We present the formal definition of MSQ. A method is investigated for the purpose of efficient processing of MSQ. We evaluated the behavior of MSQ problem and our query algorithm with both synthetic data and real data. The results show that the knowledge revealed by MSQ corresponds with the subjective feeling of similarity based on singular interest.

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Author information

Authors and Affiliations

  1. The State Key Laboratory of Intelligent Technology and System Computer Science and Technology Dept., TsingHua University, BeiJing, China, 100084

    Xiao-ming Jin, Yuchang Lu & Chunyi Shi

Authors
  1. Xiao-ming Jin
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  2. Yuchang Lu
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  3. Chunyi Shi
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Information Systems, The University of Hong Kong, Pokfulam, Hong Kong China

    David Cheung

  2. CSIRO Mathematical and Information Sciences, GPO Box 664, Canberra, ACT 2601, Australia

    Graham J. Williams

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong China

    Qing Li

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© 2001 Springer-Verlag Berlin Heidelberg

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Jin, Xm., Lu, Y., Shi, C. (2001). Micro Similarity Queries in Time Series Database. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_38

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  • DOI: https://doi.org/10.1007/3-540-45357-1_38

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41910-5

  • Online ISBN: 978-3-540-45357-4

  • eBook Packages: Springer Book Archive

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