Multi-Query Optimization of Incrementally Evaluated Sliding-Window Aggregations

Online analytics, in most advanced scientific, business, and social media applications, rely heavily on the efficient execution of large numbers of Aggregate Continuous Queries ( ACQs ). ACQs continuously aggregate streaming data and periodically produce results such as max or average over a given window of the latest data. It has been shown that it is beneficial to use Incremental Evaluation ( IE ) for re-using calculations performed over parts of the ACQ window, and to share them in multi-query ( MQ ) environments among certain sets of ACQs . In this work, we re-examine how the principle of sharing is applied in IE techniques as well as in MQ optimizers. We provide an extensive taxonomy of IE techniques and a new approach of using the state-of-the-art IE techniques as part of MQ optimizers in a way that reduces the execution plan costs by up to 270,000x. We evaluate all of our solutions both theoretically and experimentally using both real and synthetic datasets.