An exact and O(1) time heaviest and lightest hitters algorithm for sliding-window data streams

TL;DR

This paper introduces a novel algorithm that efficiently computes exact heaviest and lightest hitters in sliding-window data streams with constant time complexity for updates and queries, outperforming previous approximate methods.

Contribution

The paper presents the first exact, constant-time algorithm for both heaviest and lightest hitters in sliding windows, supporting simultaneous computation without overhead.

Findings

Algorithm achieves O(1) update and query time with high probability.

Supports simultaneous computation of heaviest and lightest hitters.

Experimental results verify theoretical efficiency and accuracy.

Abstract

In this work we focus on the problem of finding the heaviest-k and lightest-k hitters in a sliding window data stream. The most recent research endeavours have yielded an epsilon-approximate algorithm with update operations in constant time with high probability and O(1/epsilon) query time for the heaviest hitters case. We propose a novel algorithm which for the first time, to our knowledge, provides exact, not approximate, results while at the same time achieves O(1) time with high probability complexity on both update and query operations. Furthermore, our algorithm is able to provide both the heaviest-k and the lightest-k hitters at the same time without any overhead. In this work, we describe the algorithm and the accompanying data structure that supports it and perform quantitative experiments with synthetic data to verify our theoretical predictions.