Index Interpolation: An Approach to Subsequence Matching Supporting Normalization Transform in Time-Series Databases

Abstract

In this paper, we propose a subsequence matching algorithm that supports normalization transform in timeseries databases. Normalization transform enables finding sequences with similar fluctuation patterns although they are not close to each other before the normalization transform. Application of the existing whole matching algorithm supporting normalization transform to the subsequence matching is feasible, but requires an index for every possible length of the query sequence causing serious overhead on both storage space and update time. The proposed algorithm generates indexes only for a small number of different lengths of query sequences. For subsequence matching it selects the most appropriate index among them. We can obtain better search performance by using more indexes. We call our approach index interpolation. We formally prove that the proposed algorithm does not cause false dismissal. For performance evaluation, we have conducted experiments using the indexes for only five different lengths out of the lengths 256 ~ 512 of the query sequence. The results show that the proposed algorithm outperforms the sequential scan by up to 14.6 times on the average when the selectivity of the query is 10-5