SPASS: scalable event stream processing leveraging sharing opportunities: poster

M. Ray, Chuan Lei, Elke A. Rundensteiner
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Abstract

Complex Event Processing (CEP) offers high-performance event analytics in time-critical decision-making applications. Yet supporting high-performance event processing has become increasingly difficult due to the increasing size and complexity of event pattern workloads. In this work, we propose the SPASS framework that leverages time-based event correlations among queries for sharing computation tasks among sequence queries in a workload. We show the NP-hardness of our CEP pattern sharing problem by reducing it from the Minimum Substring Cover problem. The SPASS system finds a shared pattern plan in polynomial-time covering all sequence patterns while still guaranteeing an optimality bound. Further, the SPASS system assures concurrent maintenance and reuse of sub-patterns in the shared pattern plan. Our experimental evaluation confirms that the SPASS framework achieves over 16-fold performance gain compared to the state-of-the-art solutions.
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SPASS:利用共享机会的可伸缩事件流处理:海报
复杂事件处理(CEP)在时间关键型决策应用程序中提供高性能事件分析。然而,由于事件模式工作负载的规模和复杂性不断增加,支持高性能事件处理变得越来越困难。在这项工作中,我们提出了SPASS框架,该框架利用查询之间基于时间的事件相关性,在工作负载中的序列查询之间共享计算任务。我们通过简化最小子串覆盖问题来证明我们的CEP模式共享问题的np -硬度。SPASS系统在多项式时间内找到一个覆盖所有序列模式的共享模式计划,同时保证最优性边界。此外,SPASS系统确保共享模式计划中的子模式的并发维护和重用。我们的实验评估证实,与最先进的解决方案相比,SPASS框架实现了超过16倍的性能增益。
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