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Grouping Methods for Pattern Matching in Probabilistic Data Streams

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Database Systems for Advanced Applications (DASFAA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9049))

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Abstract

In recent years, complex event processing has attracted considerable interest in research and industry.Pattern matching is used to find complex events in data streams. In probabilistic data streams, however, the system may find multiple matches in a given time interval. This may result in inappropriate matches, because multiple matches may correspond to a single event. We therefore propose grouping methods of matches for probabilistic data streams, and call such merged matches a group. We describe the definitions and generation methods of groups, propose an efficient approach for calculating an occurrence probability of a group, and compare the proposed approach with a naïve one by experiment. The results demonstrate the properties and effectiveness of the proposed method.

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References

  1. Agrawal, J., Diao, Y., Gyllstrom, D., Immerman, N.: Efficient pattern matching over event streams. In: Proc. ACM SIGMOD, pp. 147–160 (2008)

    Google Scholar 

  2. Akdere, M., Çetintemel, U., Tatbul, N.: Plan-based complex event detection across distributed sources. Proc. VLDB Endow. 1(1), 66–77 (2008)

    Article  Google Scholar 

  3. Chandramouli, B., Goldstein, J., Maier, D.: High-performance dynamic pattern matching over disordered streams. Proc. VLDB Endow. 3(1–2), 220–231 (2010)

    Article  Google Scholar 

  4. Demers, A., Gehrke, J., Panda, B.: Cayuga: A general purpose event monitoring system. In: Proc. CIDR, pp. 412–422 (2007)

    Google Scholar 

  5. Gyllstrom, D., Agrawal, J., Diao, Y., Immerman, N.: On supporting Kleene closure over event streams. In: Proc. ICDE, pp. 1391–1393 (2008)

    Google Scholar 

  6. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Comput. Surv. 31(3), 264–323 (1999)

    Article  Google Scholar 

  7. Letchner, J., Ré, C., Balazinska, M., Philipose, M.: Access methods for Markovian streams. In: Proc. ICDE, pp. 246–257 (2009)

    Google Scholar 

  8. Letchner, J., Ré, C., Balazinska, M., Philipose, M.: Approximation trade-offs in Markovian stream processing: An empirical study. In: Proc. ICDE, pp. 936–939 (2010)

    Google Scholar 

  9. Li, Z., Ge, T., Chen, C.X.: \(\varepsilon \)-matching: Event processing over noisy sequences in real time. In: Proc. ACM SIGMOD, pp. 601–612 (2013)

    Google Scholar 

  10. Liu, M., Golovnya, D., Rundensteiner, E.A., Claypool, K.T.: Sequence pattern query processing over out-of-order event streams. In: Proc. ICDE, pp. 784–795 (2009)

    Google Scholar 

  11. Majumder, A., Rastogi, R., Vanama, S.: Scalable regular expression matching on data streams. In: Proc. ACM SIGMOD, pp. 161–172 (2008)

    Google Scholar 

  12. Mei, Y., Madden, S.: ZStream: A cost-based query processor for adaptively detecting composite events. In: Proc. ACM SIGMOD, pp. 193–206 (2009)

    Google Scholar 

  13. Mozafari, B., Zeng, K., Zaniolo, C.: High-performance complex event processing over XML streams. In: Proc. ACM SIGMOD, pp. 253–264 (2012)

    Google Scholar 

  14. Qi, Y., Cao, L., Ray, M., Rundensteiner, E.A.: Complex event analytics: Online aggregation of stream sequence patterns. In: Proc. ACM SIGMOD, pp. 229–240 (2014)

    Google Scholar 

  15. Ré, C., Letchner, J., Balazinksa, M., Suciu, D.: Event queries on correlated probabilistic streams. In: Proc. ACM SIGMOD, pp. 715–728 (2008)

    Google Scholar 

  16. Woods, L., Teubner, J., Alonso, G.: Complex event detection at wire speed with FPGAs. Proc. VLDB Endow. 3(1–2), 660–669 (2010)

    Article  Google Scholar 

  17. Wu, E., Diao, Y., Rizvi, S.: High-performance complex event processing over streams. In: Proc. ACM SIGMOD, pp. 407–418 (2006)

    Google Scholar 

  18. Zhang, H., Diao, Y., Immerman, N.: Recognizing patterns in streams with imprecise timestamps. Proc. VLDB Endow. 3(1–2), 244–255 (2010)

    Article  Google Scholar 

  19. Zhang, H., Diao, Y., Immerman, N.: On complexity and optimization of expensive queries in complex event processing. In: Proc. ACM SIGMOD, pp. 217–228 (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Graduate School of Information Science, Nagoya University, Nagoya, Japan

    Kento Sugiura & Yoshiharu Ishikawa

  2. Institute of Innovation for Future Society, Nagoya University, Nagoya, Japan

    Yuya Sasaki

Authors
  1. Kento Sugiura
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  2. Yoshiharu Ishikawa
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  3. Yuya Sasaki
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Corresponding author

Correspondence to Kento Sugiura .

Editor information

Editors and Affiliations

  1. Universität München, München, Germany

    Matthias Renz

  2. University of Southern California, Los Angeles, USA

    Cyrus Shahabi

  3. University of Queensland, Brisbane, Australia

    Xiaofang Zhou

  4. Monash University, Clayton, Australia

    Muhammad Aamir Cheema

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© 2015 Springer International Publishing Switzerland

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Sugiura, K., Ishikawa, Y., Sasaki, Y. (2015). Grouping Methods for Pattern Matching in Probabilistic Data Streams. In: Renz, M., Shahabi, C., Zhou, X., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9049. Springer, Cham. https://doi.org/10.1007/978-3-319-18120-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-18120-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18119-6

  • Online ISBN: 978-3-319-18120-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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