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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
10262
Venue
VLDB
Year
2011
Pagerank
5.6216111e-05
Overall Rank
5,220 | 63.69%
DOI
-

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