  Streaming systems evaluate massive workloads of event sequence aggregation
queries. State-of-the-art approaches suffer from long delays caused by not
sharing intermediate results of similar queries and by constructing event
sequences prior to their aggregation. To overcome these limitations, our Shared
Online Event Sequence Aggregation (Sharon) approach shares intermediate
aggregates among multiple queries while avoiding the expensive construction of
event sequences. Our Sharon optimizer faces two challenges. One, a sharing
decision is not always beneficial. Two, a sharing decision may exclude other
sharing opportunities. To guide our Sharon optimizer, we compactly encode
sharing candidates, their benefits, and conflicts among candidates into the
Sharon graph. Based on the graph, we map our problem of finding an optimal
sharing plan to the Maximum Weight Independent Set (MWIS) problem. We then use
the guaranteed weight of a greedy algorithm for the MWIS problem to prune the
search of our sharing plan finder without sacrificing its optimality. The
Sharon optimizer is shown to produce sharing plans that achieve up to an
18-fold speed-up compared to state-of-the-art approaches.
