Subsequence matching method using duality in constructing windows in time-series databases

Abstract

A subsequence matching method in time-series databases, reduces the number of points stored in the multidimensional index and can store individual points directly in the index by dividing the data sequence into disjoint windows using duality in constructing windows. The method reduces false alarms and improves performance by searching the index using the individual points that represent sliding windows of the query sequence and by comparing the points used in the query and the points stored in the index. Moreover, the method can create the index much faster than the previous method by reducing the number of calls to the feature extraction function that is a major part of CPU overhead in the index creation.