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• W2000482994 abstract "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 join size estimation (SSJ) problem and can handle more interesting cases such as TF-IDF vectors. One of the key challenges in similarity join size estimation is that the join size can change dramatically depending on the input similarity threshold. We propose a sampling based algorithm that uses Locality-Sensitive-Hashing (LSH). The proposed algorithm LSH-SS uses an LSH index to enable effective sampling even at high thresholds. We compare the proposed technique with random sampling and the state-of-the-art technique for SSJ (adapted to VSJ) and demonstrate LSH-SS offers more accurate estimates throughout the similarity threshold range and small variance using real-world data sets." @default.
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• W2000482994 date "2011-03-01" @default.
• W2000482994 modified "2023-09-23" @default.
• W2000482994 title "Similarity join size estimation using locality sensitive hashing" @default.
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• W2000482994 doi "https://doi.org/10.14778/1978665.1978666" @default.
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