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Similarity Search in Metric Databases through Hashing


Claudio Gennaro, Pasquale Savino, and Pavel Zezula

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Abstract

A novel access structure for similarity search in metric data-bases, called Similarity Hashing (SH), is proposed. It is a multi-level hash structure, consisting of search-separable bucket sets on each level. The structure supports easy insertion and bounded search costs, because at most one bucket needs to be accessed at each level for range queries up to a pre-defined value of search radius. At the same time, the pivot-based strategy significantly reduces the number of distance computations. Contrary to tree organizations, the SH structure is suitable for distributed and parallel implementations.


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