A Prediction Method Based on Complex Event Processing for Cyber Physical System

  • Shaofeng Geng
  • Xiaoxi Guo
  • Jia Zhang
  • Yongheng Wang
  • Renfa Li
  • Binghua Song
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 747)


For flow prediction in intelligent traffic system, one certain model cannot get excellent performance under different environments. Predicting models should also be updated according to data stream. In order to resolve these problems, a prediction method based on complex event processing was proposed. With fuzzy ontology to model historical event context and context clustering to partition events, this method could learn Bayesian network models according to different data during complex event processing. Appropriate Bayesian network model or combination of Bayesian network models could be provided by this method for real-time prediction and analysis of current context of events. The experimental result shows that this method can process events stream of Cyber Physical System (CPS) effectively and has favorable prediction performance.


Cyber Physical System Big data Complex event processing Bayesian network 



The work of this paper is sponsored by the National Natural Science Foundation of China (Grant No. 61371116) and Natural Science Foundation of Fujian Province (Grant No. 2015J01264).


  1. 1.
    Luckham, D.C.: The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison Wesley, Boston (2002)Google Scholar
  2. 2.
    Wang, Y.H., Cao, K., Zhang, X.M.: Complex event processing over distributed probabilistic event streams. In: Proceedings of the International Conference on Fuzzy Systems and Knowledge Discovery, China, pp. 1808–1821. IEEE (2012)Google Scholar
  3. 3.
    Etzion, O., Niblett, P.: Event Processing in Action. Manning Publications, Greenwich (2010)Google Scholar
  4. 4.
    Wang, J., Deng, W., Guo, Y.: New Bayesian combination method for short-term traffic flow forecasting. Transp. Res. Part C Emerg. Technol. 43, 79–94 (2014)CrossRefGoogle Scholar
  5. 5.
    Cao, K., Wang, Y., Li, R., et al.: A distributed context-aware complex event processing method for Internet of Things. J. Comput. Res. Develop. 50(6), 1163–1176 (2013)Google Scholar
  6. 6.
    Rodriguez, A., Alessandro, L.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)CrossRefGoogle Scholar
  7. 7.
    Verbeek, J., Vlassis, N., Kröse, B.: Efficient greedy learning of Gaussian mixtures. Neural Comput. 15(2), 469–485 (2003)CrossRefzbMATHGoogle Scholar
  8. 8.
    Castillo, E., Menéndez, J.M., Sánchez-Cambronero, S.: Predicting traffic flow using Bayesian networks. Transp. Res. Part B Methodological 42(5), 482–509 (2008)CrossRefGoogle Scholar
  9. 9.
    Pascale, A., Nicoli, M.: Adaptive Bayesian network for traffic flow prediction. In: Proceedings of the Statistical Signal Processing Workshop, France, pp. 177–180. IEEE (2011)Google Scholar
  10. 10.
    Sun, S., Zhang, C., Yu, G.A.: Bayesian network approach to traffic flow forecasting. IEEE Trans. Intell. Transp. Syst. 7(1), 124–132 (2006)CrossRefGoogle Scholar
  11. 11.
    Behrisch, M., Bieker, L., Erdmann, J., et al.: Sumo - simulation of urban mobility: an overview. In: Proceedings of the Third International Conference on Advances in System Simulation, SIMUL, Spain, pp. 63–68 (2011)Google Scholar
  12. 12.
    Samaranayake, S., Blandin, S., Bayen, A.: Learning the dependency structure of highway networks for traffic forecast. Mol. Pharmacol. 62(1), 5983–5988 (2011)Google Scholar
  13. 13.
    Geng, S., Wang, Y., Li, R.: The research of a proactive complex events processing method. J. Commun. 37(9), 111–120 (2016)Google Scholar
  14. 14.
    Huang, W., Song, G., Hong, H., et al.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191–2201 (2014)CrossRefGoogle Scholar
  15. 15.
    Kerner, B.S.: Three-phase traffic theory and highway capacity. Phys. A 333(1), 379–440 (2004)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Shaofeng Geng
    • 1
    • 2
  • Xiaoxi Guo
    • 1
  • Jia Zhang
    • 1
  • Yongheng Wang
    • 2
  • Renfa Li
    • 2
  • Binghua Song
    • 2
  1. 1.Jimei UniversityXiamenChina
  2. 2.Hunan UniversityChangshaChina

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