. . . "Worcester Polytechnic Institute" . . "2013" . "http://rightsstatements.org/vocab/InC/1.0/" . . "Rundensteiner, Elke A." . "MS" . "Rundensteiner, Elke A." . "Etd" . "2019-06-29T15:16:48.387913005+00:00"^^ . "Computer Science" . "etd-010213-102247" . "English" . "depositor@wpi.edu" . . "2013-01-02" . "Qi, Yingmei" . "2012-12-14" . . "High Performance Analytics in Complex Event Processing" . "Thesis" . "Optimizer" . "Complex Event Processing" . "Aggregation" . "Efficiency" . "Cost Model" . . "Complex Event Processing (CEP) is the technical choice for high performance analytics in time-critical decision-making applications. Although current CEP systems support sequence pattern detection on continuous event streams, they do not support the computation of aggregated values over the matched sequences of a query pattern. Instead, aggregation is typically applied as a post processing step after CEP pattern detection, leading to an extremely inefficient solution for sequence aggregation. Meanwhile, the state-of-art aggregation techniques over traditional stream data are not directly applicable in the context of the sequence-semantics of CEP. In this paper, we propose an approach, called A-Seq, that successfully pushes the aggregation computation into the sequence pattern detection process. A-Seq succeeds to compute aggregation online by dynamically recording compact partial sequence aggregation without ever constructing the to-be-aggregated matched sequences. Techniques are devised to tackle all the key CEP- specific challenges for aggregation, including sliding window semantics, event purging, as well as sequence negation. For scalability, we further introduce the Chop-Connect methodology, that enables sequence aggregation sharing among queries with arbitrary substring relationships. Lastly, our cost-driven optimizer selects a shared execution plan for effectively processing a workload of CEP aggregation queries. Our experimental study using real data sets demonstrates over four orders of magnitude efficiency improvement for a wide range of tested scenarios of our proposed A-Seq approach compared to the state-of-art solutions, thus achieving high-performance CEP aggregation analytics." . . "ActiveFedora::Aggregation::ListSource" . . . . .