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Entropy-Learned Hashing: Constant Time Hashing with Controllable Uniformity

Summary: Entropy-Learned Hashing models input entropy to build data-specific hash functions, cutting unnecessary randomness extraction and computation while preserving uniform output. Evaluations on hash tables, Bloom filters, and partitioning show 3.7–14x throughput gains over best-in-class implementations. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6408
Venue
SIGMOD
Year
2022
Pagerank
5.3524255e-05
Overall Rank
8,880 | 39.08%
DOI
10.1145/3514221.3517894

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{hentschel_sigmod22,
        title = {{Entropy-Learned Hashing: Constant Time Hashing with Controllable Uniformity}},
        author = {Hentschel, Brian and Sirin, Utku and Idreos, Stratos},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517894},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517894},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
10,025 VIP Hashing - Adapting to Skew in Popularity of Data on the Fly 2022 VLDB 5.1757914e-05
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Outgoing Citations (Sorted by Pagerank)

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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