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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Bencomo, N., France, R., Cheng, B.H.C., Aßmann, U.: Models@run.time: Foundations, Applications, and Roadmaps. Springer, Heidelberg (2014)
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)
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)
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)
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)
Ihler, A., Hutchins, J., Smyth, P.: Learning to detect events with Markov-modulated Poisson processes. ACM Trans. Knowl. Disc. Data 1(3), 13 (2007)
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)
Kephart, J., Chess, D.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)
Kim, E., Helal, S., Cook, D.: Human activity recognition and pattern discovery. IEEE Pervasive Comput. 9(1), 48–53 (2010)
Kulkarni, V.: Modeling and Analysis of Stochastic Systems. Chapman & Hall, Boston (1995)
Rasch, K.: An unsupervised recommender system for smart homes. J. Ambient Intell. Smart Environ. 6(1), 21–37 (2014)
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)
Salfner, F., Lenk, M., Malek, M.: A survey of online failure prediction methods. ACM Comput. Surv. 42(3), 10: 1–10: 42 (2010)
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 2007
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)
Whitt, W.: Approximating a point process by a renewal process, I: two basic methods. Oper. Res. 30(1), 125–147 (1982)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Biagi, M., Carnevali, L., Paolieri, M., Patara, F., Vicario, E. (2016). A Stochastic Model-Based Approach to Online Event Prediction and Response Scheduling. In: Fiems, D., Paolieri, M., Platis, A. (eds) Computer Performance Engineering. EPEW 2016. Lecture Notes in Computer Science(), vol 9951. Springer, Cham. https://doi.org/10.1007/978-3-319-46433-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-46433-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46432-9
Online ISBN: 978-3-319-46433-6
eBook Packages: Computer ScienceComputer Science (R0)