Speed Up of Index Creation for Time-Series Similarity Search with Handling Distortions

In this paper, we address the problem of constructing a multidimensional index for time-series similarity search with handling distortions, called distortion-free subsequence matching. A naive algorithm for index construction in distortion-free subsequence matching is a very time-consuming process since it generates a huge number of data subsequences to consider all possible positions and all possible query lengths. In this paper, we formally analyze the index construction step and discuss how to improve the performance of each step. To improve the performance, we present a concept of DF-bucket, which stores the intermediate results and reuse them repeatedly in the next steps. We also present a novel notion of store-and-reuse principle, and using the principle we build a multidimensional index much faster than a naive algorithm.