EVIS: A Fast and Scalable Episode Matching Engine for Massively Parallel Data Streams

  • Shinichiro Tago
  • Tatsuya Asai
  • Takashi Katoh
  • Hiroaki Morikawa
  • Hiroya Inakoshi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7239)

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.

Keywords

Event Type Operation Sequence Real World Dataset Event Stream Interval Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shinichiro Tago
    • 1
  • Tatsuya Asai
    • 1
  • Takashi Katoh
    • 1
  • Hiroaki Morikawa
    • 1
  • Hiroya Inakoshi
    • 1
  1. 1.Fujitsu Laboratories Ltd.KawasakiJapan

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