The Taub Faculty of Computer Science Events and Talks

The ability to promptly and efficiently detect arbitrarily complex patterns in massive real-time data streams is a crucial requirement for a wide range of modern applications. The ever-growing scale of these applications and the sophistication of the patterns involved makes it imperative to employ advanced solutions that can optimize pattern detection. One of the most prominent and well-established ways to achieve the above goal is to apply complex event processing (CEP) in a parallel manner, using a multi-core and/or a distributed environment. However, the inherent tightly coupled nature of CEP severely limits the scalability of the parallelization methods currently available. We introduce a novel parallelization mechanism for efficient complex event processing over data streams. This mechanism is based on a hybrid two-tier model combining multiple layers of parallelism. It thus allows for high scalability and fine-grained load balancing, while significantly reducing synchronization overhead. An extensive experimental evaluation on multiple real-life datasets shows that our approach consistently outperforms state-of-the-art CEP parallelization methods by a factor of up to two orders of magnitude.