시계열 서브시퀀스 매칭을 위한 최적의 다중 인덱스 구성 방안

초록

Abstract A time-series database is a set of time-series data sequences, each of which is a list of changing values of the object in a given period of time. Subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a time series database. This paper addresses a performance issue of time series subsequence matching. First, we quantitatively examine the performance degradation caused by the window size efect, and then show that the performance of subsequence matching with a single index is not satisfactory in real applications. We argue that index interpolation is fairly useful to resolve this problem. The index interpolation performs subsequence matching by selecting the most appropriate one from multiple indexes built on windows of their inherent sizes. For index interxolation, we first decide the sizes of windows for multiple indexes to be built. In this paper, we solve the problem of selecting optimal window sizes in the perspective of physical database design. For this given a set of query sequences to be performed in a target time-series database and a set of window sizes for building multiple indexes, we devise a formula that estimates the cost of all the subseruence matchings. Based on this formula, we propose an algorithen that determines the optimal window sites for maximizing

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