Abstract
Subsequence matching is a fundamental task in mining time series data. The UCR Suite approach can deal with normalized subsequence matching problem (NSM), but it needs to scan full time series. In this paper, we propose to deal with the subsequence matching problem based on a simple series synopsis, the mean values of the disjoint windows. We propose a novel problem, named constrained normalized subsequence matching problem (cNSM), which adds some constraints to NSM problem. We propose a query processing approach, named MVS-match, to process the cNSM query efficiently. The experimental results verify the effectiveness and efficiency of our approach.
The work is supported by the Ministry of Science and Technology of China, National Key Research and Development Program (2016YFB1000700), NSFC (61672163, 61170006), and National Key Basic Research Program of China under No. 2015CB358800.
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Feng, K., Wu, J., Wang, P., Pan, N., Wang, W. (2019). MVS-match: An Efficient Subsequence Matching Approach Based on the Series Synopsis. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_47
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DOI: https://doi.org/10.1007/978-3-030-18590-9_47
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