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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)

Paper ID
7429
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,238 | 29.76%
DOI
10.1145/3802056

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Authors

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}
}

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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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