  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.
