Towards an Extensible Efficient Event Processing Kernel

Mohammad Sadoghi.

In SIGMOD PhD Symposium, 2012.


The efficient processing of large collections of patterns (Boolean expressions, XPath expressions, or continuous SQL queries) over data streams plays a central role in major data intensive applications ranging from user-centric processing and personalization to real-time data analysis. On the one hand, emerging user-centric applications, including computational advertising and selective information dissemination, demand determining and presenting to an end-user only the most relevant content that is both user-consumable and suitable for limited screen real estate of target (mobile) devices. We achieve these user-centric requirements through novel high-dimensional indexing structures and (parallel) algorithms. On the other hand, applications in real-time data analysis, including computational finance and intrusion detection, demand meeting stringent subsecond processing requirements and providing high-frequency and low-latency event processing over data streams. We achieve real-time data analysis requirements by leveraging reconfigurable hardware -- FPGAs -- to sustain line-rate processing by exploiting unprecedented degrees of parallelism and potential for pipelining, only available through custom-built, application-specific, and low-level logic design. Finally, we conduct a comprehensive evaluation to demonstrate the superiority of our proposed techniques in comparison with state-of-the-art algorithms designed for event processing.


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