Aggregate window computations lie at the core of online analytics in both academic and industrial applications. To efficiently compute sliding windows, the state-of-the-art algorithms utilize incremental processing that avoids the recomputation of window results from scratch. In this paper, we propose a novel algorithm, called SlideSide, that extendsTwoStacks for multiple concurrent aggregate queries over the same data stream. Our approach uses different yet similar processing schemes for invertible and non-invertible functions and exhibits up to 2x better throughput compared to the state-of-the-art incremental techniques in a multi-query environment.