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-ofart 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.

Complex event analytics systems continuously evaluate massive workloads of pattern queries on high volume event streams to detect and extract complex events of interest to the application. Such time-critical stream-based applications range from real-time fraud detection to personalized health monitoring. Achieving near real-time system responsiveness when processing these workloads composed of complex event pattern queries is their main challenge. In this article, we first review several unique optimization opportunities that we have identified for complex event analytics. We then introduce a family of optimization strategies that consider event correlation over time to maximally leverage sharing opportunities in event pattern detection and aggregation. Lastly, we describe the event-stream transaction model we designed to ensure high performance shared pattern processing on modern multi-core architectures.

Complex Event Processing (CEP) is a technology of choice for high performance analytics in time-critical decision-making applications. Yet while effective technologies for complex pattern detection on continuous event streams have been developed, the problem of scalable online aggregation of such patterns has been overlooked. Instead, aggregation is typically applied as a post processing step after CEP pattern detection, leading to an extremely ineffective solution. In this paper, we demonstrate that CEP aggregation can be pushed into the sequence construction process. Based on this insight our A-Seq strategy successfully aggregates sequence pattern online without ever constructing sequence matches. This drives down the complexity of the CEP aggregation problem from polynomial to linear. We further extend our A-Seq strategy to support the shared processing of concurrent CEP aggregation queries. The A-Seq solution is shown to achieve over four orders of magnitude performance improvement for a wide range of tested scenarios compared to the state-of-the-art solution.