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Similarity Join Size Estimation using Locality Sensitive Hashing

Summary: Introduces LSH-SS, a sampling-based VSJ estimator leveraging Locality-Sensitive Hashing to enable accurate sampling at high similarity thresholds, generalizing SSJ to vector representations (e.g., TF-IDF). Empirical results show LSH-SS delivers higher accuracy and lower variance than random sampling and an adapted SSJ baseline across thresholds on real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10453
Venue
VLDB
Year
2011
Pagerank
6.4752373e-05
Overall Rank
4,857 | 66.68%
DOI
10.14778/2212351.2212357

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Authors

BibTeX Citation

@article{lee_vldb11,
        title = {{Similarity Join Size Estimation using Locality Sensitive Hashing}},
        author = {Lee, Hongrae and Ng, Raymond T. and Shim, Kyuseok},
        journal = {PVLDB},
        series = {{VLDB} '11},
        volume = {4},
        number = {6},
        pages = {338--349},
        doi = {10.14778/2212351.2212357},
        url = {https://doi.org/10.14778/2212351.2212357},
        year = {2011}
}

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