Aggregate window computations lie at the core of online analyt-ics in both academic and industrial applications. To efficientlycompute sliding windows, the state-of-the-art algorithms utilizeincremental processing that avoids the recomputation of windowresults from scratch. In this paper, we propose a novel algorithm,calledSlideSide, that extendsTwoStacksfor multiple concur-rent aggregate queries over the same data stream. Our approachuses different yet similar processing schemes for invertible andnon-invertible functions and exhibits up to 2×better through-put compared to the state-of-the-art incremental techniques in amulti-query environment.