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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, J., Diao, Y., Gyllstrom, D., Immerman, N.: Efficient pattern matching over event streams. In: SIGMOD, pp. 147–160 (2008)Google Scholar
  2. 2.
    Arasu, A., Cherniack, M., Galvez, E., Maier, D., Maskey, A.S., Ryvkina, E., Stonebraker, M., Tibbetts, R.: Linear road: a stream data management benchmark. In: VLDB, pp. 480–491 (2004)Google Scholar
  3. 3.
    Brenna, L., Demers, A., Gehrke, J., Hong, M., Ossher, J., Panda, B., Riedewald, M., Thatte, M., White, W.: Cayuga: a high-performance event processing engine. In: SIGMOD, pp. 1100–1102 (2007)Google Scholar
  4. 4.
    Carney, D., Çetintemel, U., Cherniack, M., Convey, C., Lee, S., Seidman, G., Stonebraker, M., Tatbul, N., Zdonik, S.: Monitoring streams: a new class of data management applications. In: VLDB, pp. 215–226 (2002)Google Scholar
  5. 5.
    Chakravarthy, S., Krishnaprasad, V., Anwar, E., Kim, S.K.: Composite events for active databases: Semantics, contexts and detection. In: VLDB, pp. 606–617 (1994)Google Scholar
  6. 6.
    Das, G., Fleischer, R., Gasieniec, L., Gunopulos, D., Kärkkäinen, J.: Episode Matching. In: Hein, J., Apostolico, A. (eds.) CPM 1997. LNCS, vol. 1264, pp. 12–27. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  7. 7.
    Dayal, U., Blaustein, B., Buchmann, A., Chakravarthy, U., Hsu, M., Ledin, R., McCarthy, D., Rosenthal, A., Sarin, S., Carey, M.J., Livny, M., Jauhari, R.: The hipac project: combining active databases and timing constraints. SIGMOD Rec. 17, 51–70 (1998)Google Scholar
  8. 8.
  9. 9.
    Gehani, N.H., Jagadish, H.V.: Ode as an active database: Constraints and triggers. In: VLDB, pp. 327–336 (1991)Google Scholar
  10. 10.
    Katoh, T., Arimura, H., Hirata, K.: Mining Frequent k-Partite Episodes from Event Sequences. In: Nakakoji, K., Murakami, Y., McCready, E. (eds.) JSAI-isAI 2009. LNCS (LNAI), vol. 6284, pp. 331–344. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Lee, E.A.: Cyber physical systems: Design challenges. In: ISORC, pp. 363–369 (2008)Google Scholar
  12. 12.
    Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1(3), 259–289 (1997)CrossRefGoogle Scholar
  13. 13.
    Mei, Y., Madden, S.: Zstream: a cost-based query processor for adaptively detecting composite events. In: SIGMOD, pp. 193–206 (2009)Google Scholar
  14. 14.
    Tatti, N., Cule, B.: Mining closed episodes with simultaneous events. In: SIGKDD, pp. 1172–1180 (2011)Google Scholar
  15. 15.
    White, W., Riedewald, M., Gehrke, J., Demers, A.: What is “next” in event processing? In: PODS, pp. 263–272 (2007)Google Scholar

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

Personalised recommendations