MVS-match: An Efficient Subsequence Matching Approach Based on the Series Synopsis

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.