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Finding Heavy-Hitters with Optimal State Changes

Summary: Streaming algorithm for ε-ℓ_k heavy-hitters with drastically reduced state-change complexity: O(ε^{-1} n^{1-1/k} · poly(log log n) · log(1/ε)) state changes and O(1/ε^k · polylog n) space for k∈[1,2] (extension to k≥2 incurs n^{1-2/k} extra space). Shows a matching lower bound up to log log n factors and credits the improvement to a new, very simple insertion-only heavy-hitters subroutine. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID
1994
Venue
PODS
Year
2026
Pagerank
4.1905499e-05
Overall Rank
10,004 | 30.48%
DOI
10.1145/3767714

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

Rank Cited Paper Year Venue Pagerank
43 Models and Issues in Data Stream Systems 2002 PODS 0.00072660894
4,950 Optimal Bounds for Approximate Counting 2022 PODS 5.808904e-05
6,347 BPTree: an ℓ2 Heavy Hitters Algorithm Using Constant Memory 2017 PODS 5.0969206e-05
10,905 Streaming Algorithms with Few State Changes 2024 PODS 4.1905499e-05
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