The Taub Faculty of Computer Science Events and Talks

The ability to detect complex patterns in massive data streams is critical for many real-time applications. These applications must uphold low latency requirements, delivering alerts and notifications with minimal response delays. Complex event processing (CEP), a leading technology for performing this task, is suitable for the efficient and robust detection of complex patterns. However, the CEP complexity grows exponentially with respect to the length of the pattern and the intensity of the data stream. As a result, most CEP based applications that monitor frequent data are often incapable of analyzing complex patterns under real-time conditions, and thus define only simple patterns in practice. We present a novel deep learning based framework to accelerate the detection process of CEP systems. We use a neural network to filter the input stream, thereby significantly reducing the detection latency. This network is trained to efficiently filter the input based on the pattern. In parallel to running a CEP engine that processes the filtered substream, we employ CEP on the original input, which ensures a detection accuracy of 100%. Extensive experimental evaluation of several real-world datasets shows that our approach consistently improves the latency by up to a factor of 100 as compared to existing state-of-the-art CEP systems.