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Similarity joins are important operations with a broad range of applications.In this paper, we study the problem of vector similarity join size estimation(VSJ). It is a generalization of the previously studied set similarity joinsize estimation (SSJ) problem and can handle more interesting cases such asTF-IDF vectors. One of the key challenges in similarity join size estimation isthat the join size can change dramatically depending on the input similaritythreshold.  We propose a sampling based algorithm that uses theLocality-Sensitive-Hashing (LSH) scheme. The proposed algorithm LSH-SS uses anLSH index to enable effective sampling even at high thresholds. We compare theproposed technique with random sampling and the state-of-the-art technique forSSJ (adapted to VSJ) and demonstrate LSH-SS offers more accurate estimates atboth high and low similarity thresholds and small variance using real-worlddata sets.

Similarity Join Size Estimation using Locality Sensitive Hashing