Hashed Samples: Selectivity Estimators For Set Similarity Selection Queries
Summary: Hashed Samples designs selectivity estimators for weighted set similarity queries (TF-IDF/BM25) using a priori constructed samples. It avoids uniform sampling pitfalls, proves accuracy theoretically, and delivers orders-of-magnitude speedups with small space overhead compared with exact solutions. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Marios Hadjieleftheriou (AT&T)
- 2. Xiaohui Yu (York University)
- 3. Nick Koudas (University of Toronto)
- 4. Divesh Srivastava (AT&T)
BibTeX Citation
@article{hadjieleftheriou_vldb08,
title = {{Hashed Samples: Selectivity Estimators For Set Similarity Selection Queries}},
author = {Hadjieleftheriou, Marios and Yu, Xiaohui and Koudas, Nick and Srivastava, Divesh},
journal = {PVLDB},
series = {{VLDB} '08},
volume = {1},
number = {1},
pages = {201--212},
doi = {10.14778/1453856.1453883},
url = {https://doi.org/10.14778/1453856.1453883},
year = {2008}
}
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