Abstract
We propose a fast episode pattern matching engine EVIS that detects all occurrences in massively parallel data streams for an episode pattern, which represents a collection of event types in a given partial order. There should be important applications to be addressed with this technology, such as monitoring stock price movements, and tracking vehicles or merchandise by using GPS or RFID sensors. EVIS employs a variant of non-deterministic finite automata whose states are extended to maintain their activated times and activating streams. This extension allows EVIS’s episode pattern to have 1) interval constraints that enforce time-bound conditions on every pair of consequent event types in the pattern, and 2) stream constraints by which two interested series of events are associated with each other and found in arbitrary pairs of streams. The experimental results show that EVIS performs much faster than a popular CEP engine for both artificial and real world datasets, as well as that EVIS effectively works for over 100,000 streams.
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Tago, S., Asai, T., Katoh, T., Morikawa, H., Inakoshi, H. (2012). EVIS: A Fast and Scalable Episode Matching Engine for Massively Parallel Data Streams. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29035-0_15
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DOI: https://doi.org/10.1007/978-3-642-29035-0_15
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