Tight Bounds for Heavy-Hitters and Moment Estimation in the Sliding Window Model

We consider the heavy-hitters and F_p moment estimation problems in the sliding window model. For F_p moment estimation with 1 < p ≤ 2, we show that it is possible to give a (1± ε) multiplicative approximation to the F_p moment with 2/3 probability on any given window of size n using Õ(1/(ε^p)log² n + 1/(ε²)log n) bits of space. We complement this result with a lower bound showing that our algorithm gives tight bounds up to factors of log log n and log1/(ε). As a consequence of our F₂ moment estimation algorithm, we show that the heavy-hitters problem can be solved on an arbitrary window using O(1/(ε²)log² n) space which is tight.

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Publication Details

Journal
ArXiv.org
Published
2025-01-01
DOI
https://doi.org/10.4230/lipics.icalp.2025.75
Primary Topic
Probability and Risk Models
Type
article
Field-Weighted Citation Impact
0.00

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article

Tight Bounds for Heavy-Hitters and Moment Estimation in the Sliding Window Model

William Swartworth, Shiyuan Feng, David P. Woodruff
ArXiv.org
Probability and Risk Models
article

Tight Bounds for Heavy-Hitters and Moment Estimation in the Sliding Window Model

William Swartworth, Shiyuan Feng, David P. Woodruff
article en

Abstract

We consider the heavy-hitters and F_p moment estimation problems in the sliding window model. For F_p moment estimation with 1 < p ≤ 2, we show that it is possible to give a (1± ε) multiplicative approximation to the F_p moment with 2/3 probability on any given window of size n using Õ(1/(ε^p)log² n + 1/(ε²)log n) bits of space. We complement this result with a lower bound showing that our algorithm gives tight bounds up to factors of log log n and log1/(ε). As a consequence of our F₂ moment estimation algorithm, we show that the heavy-hitters problem can be solved on an arbitrary window using O(1/(ε²)log² n) space which is tight.

ArXiv.org
Office of Naval Research
Openalex Percentile: Top 54%
Probability and Risk Models
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