Fast Time Sequence Indexing for Arbitrary Lp Norms

Fast indexing in time sequence databases for similarity searching has attracted a lot of research recently. Most of the proposals, however, typically centered around the Euclidean distance and its derivatives. We examine the problem of multimodal similarity search in which users can choose the best one from multiple similarity models for their needs. In this paper, we present a novel and fast indexing scheme for time sequences, when the distance function is any of arbitrary Lp norms (p = 1; 2; : : : ;1). One feature of the proposed method is that only one index structure is needed for all Lp norms including the popular Euclidean distance (L2 norm). Our scheme achieves significant speedups over the state of the art: extensive experiments on real and synthetic time sequences show that the proposed method is comparable to the best competitor forL2 andL1 norms, but significantly (up to 10 times) faster for L1 norm.

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