Multi-Query Optimization of Incrementally Evaluated Sliding-Window Aggregations


The successful implementation of a large number of aggregate continuous queries is essential to the success of online analytics in virtually all of the most cutting-edge scientific, commercial, and social media applications ( ACQs ). ACQs continuously aggregate streaming data and produce results such as max or average over a given window of the most recent data on a periodic basis. These results can be viewed in the form of graphs. It has been demonstrated that it is beneficial to use Incremental Evaluation (IE) for reusing calculations that were performed over parts of the ACQ window, and to share them in multi-query (MQ) environments among certain sets of ACQs. This can be accomplished by reusing calculations that were performed over parts of the ACQ window. Within the scope of this study, we revisit the question of how the concept of sharing is implemented within IE strategies as well as MQ optimizers. We offer a comprehensive taxonomy of IE techniques in addition to a novel strategy for using the most advanced IE techniques as components of MQ optimizers in a manner that can cut down the costs of the execution plan by up to 270,000 times. We evaluate each of our solutions theoretically as well as experimentally, making use of real as well as simulated datasets in the process.

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