Database Paper Browser

Back to papers

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
6346
Venue
SIGMOD
Year
2022
Pagerank
4.4609699e-05
Overall Rank
8,720 | 39.34%
DOI
10.1145/3514221.3517894

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
9,858 VIP Hashing - Adapting to Skew in Popularity of Data on the Fly 2022 VLDB 4.269353e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

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

Previous Page 1 / 1 Next

Semantically Similar Papers