Fair-Count-Min: Frequency Estimation under Equal Group-wise Approximation Factor
Summary: Fair-Count-Min is a group-aware Count-Min variant that partitions columns and uses semi-uniform hashing to eliminate cross-group collisions, equalizing expected approximation factors rather than additive error. Experiments show fairness with small overhead and near-standard efficiency. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Nima Shahbazi (University of Illinois Chicago)
- 2. Stavros Sintos (University of Illinois Chicago)
- 3. Abolfazl Asudeh (University of Illinois Chicago)
BibTeX Citation
@inproceedings{shahbazi_sigmod26,
title = {{Fair-Count-Min: Frequency Estimation under Equal Group-wise Approximation Factor}},
author = {Shahbazi, Nima and Sintos, Stavros and Asudeh, Abolfazl},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802056},
url = {https://dl.acm.org/doi/10.1145/3802056},
year = {2026}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,511 | On Fair Epsilon Net and Geometric Hitting Set | 2026 | VLDB | 5.093636e-05 |
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