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.
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