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Together is Better: Heavy Hitters Quantile Estimation

Summary: Per-ID quantile estimation in data streams, focusing on heavy hitters to avoid per-ID sketches. SQUAD—a hybrid sampling-sketching algorithm—bootstraps quantile sketches with background sampling to lower space, with theoretical guarantees and real-trace validation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h243ab67cf5d20833
Venue
SIGMOD
Year
2023
Pagerank
5.5160692e-05
Overall Rank
7,415 | 50.30%
DOI
10.1145/3588937

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{shahout_sigmod23,
        title = {{Together is Better: Heavy Hitters Quantile Estimation}},
        author = {Shahout, Rana and Friedman, Roy and Basat, Ran Ben},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588937},
        url = {https://dl.acm.org/doi/10.1145/3588937},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
8,837 SplineSketch: Even More Accurate Quantiles with Error Guarantees 2026 SIGMOD 5.2523304e-05
10,403 Near-Optimal Per-Key Streaming Quantile Estimation 2027 SIGMOD 4.9605343e-05
10,746 Quantile Estimation with Duplicates 2026 SIGMOD 4.9605343e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

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

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