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A Frequent Sequential Pattern Based Approach for Discovering Event Correlations

  • Yunmeng CaoEmail author
  • Chen Liu
  • Yanbo Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11242)

Abstract

In an IoT environment, event correlation becomes more complex as events usually span over many interrelated sensors. This paper refines event correlations in an IoT environment, and proposes an algorithm to discover event correlations. We transform the event correlation discovery problem into a time-constrained frequent sequence mining problem. Moreover, we apply our approach in anomaly warning in a coal power plant. We have made extensive experiments to verify the effectiveness of our approach.

Keywords

Event correlation Sensor event Anomaly warning 

Notes

Acknowledgement

National Key R&D Plan (No. 2017YFC0804406); National Natural Science Foundation of China (No. 61672042); The Program for Youth Backbone Individual, supported by Beijing Municipal Party Committee Organization Department, “Research of Instant Fusion of Multi-Source and Large-Scale Sensor Data”.

References

  1. 1.
    Pourmirza, S., Dijkman, R., Grefen, P.: Correlation miner: mining business process models and event correlations without case identifiers. Int. J. Coop. Inf. Syst. 26(2), 1–32 (2017)CrossRefGoogle Scholar
  2. 2.
    Cheng, L., Van Dongen, B.F., Van Der Aalst, W.M.P.: Efficient event correlation over distributed systems. In: 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 1–10. Institute of Electrical and Electronics Engineers Inc., Madrid (2017)Google Scholar
  3. 3.
    Pourmirza, S., Dijkman, R., Grefen, P.: Correlation mining: mining process orchestrations without case identifiers. In: Barros, A., Grigori, D., Narendra, N.C., Dam, H.K. (eds.) ICSOC 2015. LNCS, vol. 9435, pp. 237–252. Springer, Heidelberg (2015).  https://doi.org/10.1007/978-3-662-48616-0_15CrossRefGoogle Scholar
  4. 4.
    Reguieg, H., Benatallah, B., Nezhad, H.R.M., Toumani, F.: Event correlation analytics: scaling process mining using mapreduce-aware event correlation discovery techniques. IEEE Trans. Serv. Comput. 8(6), 847–860 (2015)CrossRefGoogle Scholar
  5. 5.
    Friedberg, I., Skopik, F., Settanni, G., Fiedler, R.: Combating advanced persistent threats: from network event correlation to incident detection. Comput. Secur. 48, 35–57 (2015)CrossRefGoogle Scholar
  6. 6.
    Fu, S., Xu, C.: Quantifying event correlations for proactive failure management in networked computing systems. J. Parallel Distrib. Comput. 70(11), 1100–1109 (2010)CrossRefGoogle Scholar
  7. 7.
    Forkan, A.R.M., Khalil, I.: PEACE-home: probabilistic estimation of abnormal clinical events using vital sign correlations for reliable home-based monitoring. Pervasive Mob. Comput. 38, 296–311 (2017)CrossRefGoogle Scholar
  8. 8.
    Forkan, A.R.M., Khalil, I.: A probabilistic model for early prediction of abnormal clinical events using vital sign correlations in home-based monitoring. In: 14th IEEE International Conference on Pervasive Computing and Communications, pp. 1–9. Institute of Electrical and Electronics Engineers Inc., Sydney (2016)Google Scholar
  9. 9.
    Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11. Association for Computing Machinery, San Diego (2003)Google Scholar
  10. 10.
    Pei, J., Han, J., Wang, W.: Constraint-based sequential pattern mining: the pattern-growth methods. J. Intell. Inf. Syst. 28(2), 133–160 (2007)CrossRefGoogle Scholar
  11. 11.
    Mooney, C.H., Roddick, J.F.: Sequential pattern mining: approaches and algorithms. ACM Comput. Surv. 45(2), 1–39 (2013)CrossRefGoogle Scholar
  12. 12.
    Song, W., Jacobsen, H.A., Ye, C., Ma, X.: Process discovery from dependence-complete event logs. IEEE Trans. Serv. Comput. 9(5), 714–727 (2016)CrossRefGoogle Scholar
  13. 13.
    Plantevit, M., Robardet, C., Scuturici, V.M.: Graph dependency construction based on interval-event dependencies detection in data streams. Intell. Data Anal. 20(2), 223–256 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream DataNorth China University of TechnologyBeijingChina
  2. 2.Data Engineering InstituteNorth China University of TechnologyBeijingChina

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