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Approximate Distinct Counts for Billions of Datasets

Summary: Single-pass blends CountMin and HyperLogLog to estimate billions of distinct counts and enable multi-resolution aggregations. Offers correctness guarantees, tight error bounds, and consistent estimators with exact asymptotic coverage for multisets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5771
Venue
SIGMOD
Year
2019
Pagerank
6.0142553e-05
Overall Rank
5,998 | 58.85%
DOI
10.1145/3299869.3319897

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BibTeX Citation

@inproceedings{ting_sigmod19,
        title = {{Approximate Distinct Counts for Billions of Datasets}},
        author = {Ting, Daniel},
        series = {{SIGMOD} '19},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3299869.3319897},
        url = {https://dl.acm.org/doi/10.1145/3299869.3319897},
        year = {2019}
}

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