Searching Web Data using MinHash LSH
Summary: MinHash LSH applied to indexing and searching Web data, with a statistical tuning strategy for MinHash LSH. Empirical evaluation against inverted indexes and an online demo on real Web data reveal accuracy and performance tradeoffs and practical viability. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. BiChen Rao (University of Toronto)
- 2. Erkang Zhu (University of Toronto)
BibTeX Citation
@inproceedings{rao_sigmod16,
title = {{Searching Web Data using MinHash LSH}},
author = {Rao, BiChen and Zhu, Erkang},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2914838},
url = {https://dl.acm.org/doi/10.1145/2882903.2914838},
year = {2016}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,546 | PM-LSH: A Fast and Accurate LSH Framework for High-Dimensional Approximate NN Search | 2020 | VLDB | 0.00010407159 |
| 2,541 | Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science | 2018 | VLDB | 8.4500033e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,495 | LSH Ensemble: Internet-Scale Domain Search | 2016 | VLDB | 0.00010571481 |
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