A Stochastic Model-Based Approach to Online Event Prediction and Response Scheduling

  • Marco Biagi
  • Laura Carnevali
  • Marco Paolieri
  • Fulvio Patara
  • Enrico Vicario
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9951)

Abstract

In a variety of contexts, time-stamped and typed event logs enable the construction of a stochastic model capturing the sequencing and timing of observable discrete events. This model can serve various objectives including: diagnosis of the current state; prediction of its evolution over time; scheduling of response actions. We propose a technique that supports online scheduling of actions based on a prediction of the model state evolution: the model is derived automatically by customizing the general structure of a semi-Markov process so as to fit the statistics of observed logs; the prediction is updated whenever any observable event changes the current state estimation; the (continuous) time point of the next scheduled action is decided according to policies based on the estimated distribution of the time to given critical states. Experimental results are reported to characterize the applicability of the approach with respect to general properties of the statistics of observable events and with respect to a specific reference dataset from the context of Ambient Assisted Living.

References

  1. 1.
    Babaoglu, O., Jelasity, M., Montresor, A., Fetzer, C., Leonardi, S., van Moorsel, A.: The self-star vision. In: Babaoğlu, Ö., Jelasity, M., Montresor, A., Fetzer, C., Leonardi, S., Moorsel, A., Steen, M. (eds.) SELF-STAR 2004. LNCS, vol. 3460, pp. 1–20. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Bencomo, N., France, R., Cheng, B.H.C., Aßmann, U.: Models@run.time: Foundations, Applications, and Roadmaps. Springer, Heidelberg (2014)Google Scholar
  3. 3.
    Bucci, G., Carnevali, L., Ridi, L., Vicario, E.: Oris: a tool for modeling, verification and evaluation of real-time systems. Int. J. SW Tools Technol. Transf. 12(5), 391–403 (2010)CrossRefGoogle Scholar
  4. 4.
    Carnevali, L., Nugent, C., Patara, F., Vicario, E.: A continuous-time model-based approach to activity recognition for ambient assisted living. In: Campos, J., Haverkort, B.R. (eds.) QEST 2015. LNCS, vol. 9259, pp. 38–53. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  5. 5.
    Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. Man, Cybern. Part C: Appl. Rev. 42(6), 790–808 (2012)CrossRefGoogle Scholar
  6. 6.
    Horváth, A., Paolieri, M., Ridi, L., Vicario, E.: Transient analysis of non-Markovian models using stochastic state classes. Perform. Eval. 69(7–8), 315–335 (2012)CrossRefGoogle Scholar
  7. 7.
    Ihler, A., Hutchins, J., Smyth, P.: Learning to detect events with Markov-modulated Poisson processes. ACM Trans. Knowl. Disc. Data 1(3), 13 (2007)CrossRefGoogle Scholar
  8. 8.
    Katz, S., Downs, T.D., Cash, H.R., Grotz, R.C.: Progress in development of the index of ADL. The Gerontologist 10 (1 Part 1), 20–30 (1970)CrossRefGoogle Scholar
  9. 9.
    Kephart, J., Chess, D.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kim, E., Helal, S., Cook, D.: Human activity recognition and pattern discovery. IEEE Pervasive Comput. 9(1), 48–53 (2010)CrossRefGoogle Scholar
  11. 11.
    Kulkarni, V.: Modeling and Analysis of Stochastic Systems. Chapman & Hall, Boston (1995)MATHGoogle Scholar
  12. 12.
    Rasch, K.: An unsupervised recommender system for smart homes. J. Ambient Intell. Smart Environ. 6(1), 21–37 (2014)Google Scholar
  13. 13.
    Rogge-Solti, A., van der Aalst, W.M.P., Weske, M.: Discovering stochastic petri nets with arbitrary delay distributions from event logs. In: International Business Process Management Workshops, BpPM, pp. 15–27 (2013)Google Scholar
  14. 14.
    Salfner, F., Lenk, M., Malek, M.: A survey of online failure prediction methods. ACM Comput. Surv. 42(3), 10: 1–10: 42 (2010)CrossRefGoogle Scholar
  15. 15.
    Salfner, F., Malek, M.: Using hidden semi-Markov models for effective online failure prediction. In: 26th IEEE International Symposium on Reliable Distributed Systems SRDS 2007, pp. 161–174, October 2007Google Scholar
  16. 16.
    van Kasteren, T., Noulas, A., Englebienne, G., and Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of International Conference on Ubiquitous Computing, UbiComp 2008, pp. 1–9. ACM, New York (2008)Google Scholar
  17. 17.
    Whitt, W.: Approximating a point process by a renewal process, I: two basic methods. Oper. Res. 30(1), 125–147 (1982)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Marco Biagi
    • 1
  • Laura Carnevali
    • 1
  • Marco Paolieri
    • 1
  • Fulvio Patara
    • 1
  • Enrico Vicario
    • 1
  1. 1.Department of Information EngineeringUniversity of FlorenceFlorenceItaly

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