Fast time sequence indexing for arbitrary 4 norms

Document Type

Conference Proceeding

Publication Date

12-1-2000

Abstract

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 multi- modal 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 C, norms (p = 1,2, . . , 00). One feature of the proposed method is that only one index structure is needed for all C, norms including the popular Euclideán distance (C2 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 for L2 and L norms, but significantly (up to 10 times) faster for L norm.

Identifier

0008693810 (Scopus)

ISBN

[1558607153, 9781558607156]

Publication Title

Proceedings of the 26th International Conference on Very Large Data Bases VLDB 00

First Page

385

Last Page

394

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