Abstract:
Most advanced scientific, business, and social media applications use online analytics to efficiently execute large numbers of Aggregate Continuous Queries (ACQs). ACQs aggregate streaming data and periodically calculate max or average over a window of the latest data.
Incremental Evaluation (IE) is useful for reusing calculations over parts of the ACQ window and sharing them in multi-query (MQ) environments among certain ACQs. This study revisits how IE and MQ optimizers use sharing.
We provide an extensive taxonomy of IE techniques and a new way to use state-of-the-art IE techniques in MQ optimizers to reduce execution plan costs by up to 270,000x. Our solutions are tested theoretically and experimentally using real and synthetic datasets.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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