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)
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Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,884 | BPTree: an ℓ2 Heavy Hitters Algorithm Using Constant Memory | 2017 | PODS | 7.9620506e-05 |
| 8,203 | SpaceSaving±: An Optimal Algorithm for Frequency Estimation and Frequent Items in the Bounded-Deletion Model | 2022 | VLDB | 4.5596344e-05 |
| 11,440 | Frequent Elements with Witnesses in Data Streams | 2021 | PODS | 4.1945683e-05 |
| 11,562 | Timely Reporting of Heavy Hitters using External Memory | 2020 | SIGMOD | 4.1945683e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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