An Optimal Algorithm for l1-Heavy Hitters in Insertion Streams and Related Problems
Summary: Optimal l1-heavy-hitters algorithm for insertion streams: returns all items with f_i≥φm, rejects those ≤(φ−ε)m, gives |f̂_i−f_i|≤εm, uses O(ε^{-1}log(1/φ)+φ^{-1}log n+log log m) bits and O(1) updates. Matches a proved space lower bound; also yields max-frequency estimation and comparison-based variants for rank-aggregation/voting. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Arnab Bhattacharyya (Indian Institute of Science)
- 2. Palash Dey (Indian Institute of Science)
- 3. David P. Woodruff (IBM)
BibTeX Citation
@inproceedings{bhattacharyya_pods16,
address = {New York, NY, USA},
series = {{PODS} '16},
title = {{An Optimal Algorithm for l1-Heavy Hitters in Insertion Streams and Related Problems}},
url = {https://dl.acm.org/doi/10.1145/2902251.2902284},
doi = {10.1145/2902251.2902284},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Bhattacharyya, Arnab and Dey, Palash and Woodruff, David P.},
year = {2016}
}
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,778 | Data Streams with Bounded Deletions | 2018 | PODS | 5.9977531e-05 |
| 6,395 | BPTree: an ℓ2 Heavy Hitters Algorithm Using Constant Memory | 2017 | PODS | 5.7998727e-05 |
| 7,697 | SpaceSaving±: An Optimal Algorithm for Frequency Estimation and Frequent Items in the Bounded-Deletion Model | 2022 | VLDB | 5.4754508e-05 |
| 10,379 | On Sketching Trimmed Statistics | 2026 | PODS | 4.9793485e-05 |
| 11,945 | Frequent Elements with Witnesses in Data Streams | 2021 | PODS | 4.9793485e-05 |
| 12,062 | Timely Reporting of Heavy Hitters using External Memory | 2020 | SIGMOD | 4.9793485e-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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