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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
4.1945683e-05
Overall Rank
10,005 | 30.40%
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 0.00072723062
2,884 BPTree: an ℓ2 Heavy Hitters Algorithm Using Constant Memory 2017 PODS 7.9620506e-05
5,550 Optimal Bounds for Approximate Counting 2022 PODS 5.4422275e-05
10,901 Streaming Algorithms with Few State Changes 2024 PODS 4.1945683e-05
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