A robust approach to subsequence matching

A. Zheng, J. Ma, Miltiadis Petridis, J. Tang, B. Luo

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNChapterpeer-review


In terms of a general time theory which addresses time-elements as typed point-based intervals, a formal characterization of time-series and state-sequences is introduced. Based on this framework, the subsequence matching problem is specially tackled by means of being transferred into bipartite graph matching problem. Then a hybrid similarity model with high tolerance of inversion, crossover and noise is proposed for matching the corresponding bipartite graphs involving both temporal and non-temporal measurements. Experimental results on reconstructed time-series data from UCI KDD Archive demonstrate that such an approach is more effective comparing with the traditional similarity model based algorithms, promising robust techniques for lager time-series databases and real-life applications such as Content-based Video Retrieval (CBVR), etc.
Original languageEnglish
Title of host publicationSoftware Engineering Research, Management and Applications
EditorsR. Lee, N. Ishii
Place of PublicationBerlin Heidelberg
Number of pages11
ISBN (Electronic)9783642054419
ISBN (Print)9783642054402
Publication statusPublished - 2009

Publication series

NameStudies in Computational Intelligence


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