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

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