This document discusses optimizing query evaluation over streaming and distributed data to continuously obtain relevant results while maintaining system reactiveness. It proposes approaches for queries with filter clauses and top-k queries. For queries with filters, maintenance policies like Filter Update Policy and combined policies improve performance. For top-k queries, the Super-MTK+N list and Top-k+N algorithm handle changes to distributed data. The AcquaTop framework applies different maintenance policies. Experimental results show the approaches achieve more relevant and accurate results than the state-of-the-art.