Flexible Framework for Time-Series Pattern Matching over Multi-dimension Data Stream

  • Takuya Kida
  • Tomoya Saito
  • Hiroki Arimura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5433)


In this paper, we study a complex time-series pattern matching problem over a multi-dimension continuous data stream. For each data stream, a pattern is given as a sequence of predicates, which specify a sequence of element sets on the stream. The pattern matching problem over such a multi-dimension data stream, is to find all occurrences where all predicates in the patterns are satisfied. We propose a flexible and extensible framework to solve the problem, which is based on bit-parallel pattern matching method that simulates NFAs for the pattern matching efficiently by a few logical bit operations. We consider four types of data streams especially: textual, categorical, ordered, and numeric, that is, those are a sequence of strings, concepts with taxonomic information, small integers, and real numbers (or large integers), respectively. We also present the time complexities to do pattern matching for those data types.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Takuya Kida
    • 1
  • Tomoya Saito
    • 1
  • Hiroki Arimura
    • 1
  1. 1.Hokkaido UniversitySapporoJapan

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