Abstract
In this paper, we propose a method for online subsequence matching between histogram-based stream synopsis structures under the dynamic warping distance. Given a query synopsis pattern, the work continuously identifies all the matching subsequences for a stream as the histograms are generated. To effectively reduce the computation time, we design a Weighted Dynamic Time Warping (WDTW) algorithm which computes the warping distance directly between two histogram-based synopses. Our experiments on real datasets show that the proposed method significantly speeds up the pattern matching by sacrificing a little accuracy.
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Lin, SC., Yeh, MY., Chen, MS. (2010). Subsequence Matching of Stream Synopses under the Time Warping Distance. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13672-6_35
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DOI: https://doi.org/10.1007/978-3-642-13672-6_35
Publisher Name: Springer, Berlin, Heidelberg
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