SPASS: scalable event stream processing leveraging sharing opportunities: poster

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.