Data-Driven Performance Analysis of Scheduled Processes

  • Arik Senderovich
  • Andreas Rogge-Solti
  • Avigdor Gal
  • Jan Mendling
  • Avishai Mandelbaum
  • Sarah Kadish
  • Craig A. Bunnell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9253)


The performance of scheduled business processes is of central importance for services and manufacturing systems. However, current techniques for performance analysis do not take both queueing semantics and the process perspective into account. In this work, we address this gap by developing a novel method for utilizing rich process logs to analyze performance of scheduled processes. The proposed method combines simulation, queueing analytics, and statistical methods. At the heart of our approach is the discovery of an individual-case model from data, based on an extension of the Colored Petri Nets formalism. The resulting model can be simulated to answer performance queries, yet it is computational inefficient. To reduce the computational cost, the discovered model is projected into Queueing Networks, a formalism that enables efficient performance analytics. The projection is facilitated by a sequence of folding operations that alter the structure and dynamics of the Petri Net model. We evaluate the approach with a real-world dataset from Dana-Farber Cancer Institute, a large outpatient cancer hospital in the United States.


Schedule Process Parallel Task Queueing Network Queueing Station Schedule Transition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    van der Aalst, W.M.P.: Petri net based scheduling. Operations-Research-Spektrum 18(4), 219–229 (1996)zbMATHCrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P.: Process mining: Discovery, Conformance and Enhancement of Business Processes. Springer (2011)Google Scholar
  3. 3.
    van der Aalst, W.M.P.: Interval timed coloured petri nets and their analysis. In: Ajmone Marsan, M. (ed.) ICATPN 1993. LNCS, vol. 691, pp. 453–472. Springer, Heidelberg (1993) CrossRefGoogle Scholar
  4. 4.
    Ajmone Marsan, M., Conte, M., Balbo, G.: A class of generalized stochastic petri nets for the performance evaluation of multiprocessor systems. ACM Trans. Comput. Syst. 2(2), 93–122 (1984)CrossRefGoogle Scholar
  5. 5.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Communications of the ACM 26(11), 832–843 (1983)zbMATHCrossRefGoogle Scholar
  6. 6.
    Bause, F.: Queueing Petri nets-a formalism for the combined qualitative and quantitative analysis of systems. In: PNPM 1993, pp. 14–23. IEEE (1993)Google Scholar
  7. 7.
    Bause, F., Kritzinger, P.S.: Stochastic Petri Nets. Springer (1996)Google Scholar
  8. 8.
    Bolch, G., Greiner, S., de Meer, H., Trivedi, K.S.: Queueing networks and Markov chains: modeling and performance evaluation with computer science applications. John Wiley & Sons (2006)Google Scholar
  9. 9.
    Boxma, O., Koole, G., Liu, Z.: Queueing-theoretic solution methods for models of parallel and distributed systems. Statistics, and System Theory, Centrum voor Wiskunde en Informatica, Department of Operations Research (1994)Google Scholar
  10. 10.
    Chiola, G., Dutheillet, C., Franceschinis, G., Haddad, S.: Stochastic well-formed colored nets and symmetric modeling applications. IEEE Trans. Comput. 42(11), 1343–1360 (1993)CrossRefGoogle Scholar
  11. 11.
    Ibrahim, R., Whitt, W.: Real-time delay estimation based on delay history. Manufacturing and Service Operations Management 11(3), 397–415 (2009)CrossRefGoogle Scholar
  12. 12.
    Jacobson, P.A., Lazowska, E.D.: Analyzing queueing networks with simultaneous resource possession. Commun. ACM 25(2), 142–151 (1982)CrossRefGoogle Scholar
  13. 13.
    Jensen, K.: Coloured Petri nets: basic concepts, analysis methods and practical use, vol. 1. Springer (1997)Google Scholar
  14. 14.
    Juan, E.Y., Tsai, J.J., Murata, T., Zhou, Y.: Reduction methods for real-time systems using delay time petri nets. IEEE Transactions on Software Engineering 27(5), 422–448 (2001)CrossRefGoogle Scholar
  15. 15.
    Pinedo, M.L.: Planning and Scheduling in Manufacturing and Services. Springer (2005)Google Scholar
  16. 16.
    Pommereau, F.: Quickly prototyping petri nets tools with SNAKES. In: Proceedings of PNTAP 2008, pp. 1–10. ACM (2008)Google Scholar
  17. 17.
    Reiman, M.I., Simon, B.: A network of priority queues in heavy traffic: One bottleneck station. Queueing Systems 6(1), 33–57 (1990)zbMATHMathSciNetCrossRefGoogle Scholar
  18. 18.
    Rogge-Solti, A., van der Aalst, W.M.P., Weske, M.: Discovering stochastic petri nets with arbitrary delay distributions from event logs. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013 Workshops. LNBIP, vol. 171, pp. 15–27. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  19. 19.
    Rozinat, A., Mans, R.S., Song, M., van der Aalst, W.M.P.: Discovering simulation models. Information Systems 34(3), 305–327 (2009)CrossRefGoogle Scholar
  20. 20.
    Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A.: Queue mining for delay prediction in multi-class service processes. Tech. rep. (2014)Google Scholar
  21. 21.
    Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A.: Queue mining – predicting delays in service processes. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 42–57. Springer, Heidelberg (2014) Google Scholar
  22. 22.
    Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A., Kadish, S., Bunnell, C.A.: Discovery and validation of queueing networks in scheduled processes. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 417–433. Springer, Heidelberg (2015) CrossRefGoogle Scholar
  23. 23.
    Smirnov, S., Reijers, H., Weske, M., Nugteren, T.: Business process model abstraction: a definition, catalog, and survey. Distributed and Parallel Databases 30(1), 63–99 (2012)CrossRefGoogle Scholar
  24. 24.
    Vernon, M., Zahorjan, J., Lazowska, E.D.: A comparison of performance Petri nets and queueing network models. University of Wisconsin-Madison, Computer Sciences Department (1986)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Arik Senderovich
    • 1
  • Andreas Rogge-Solti
    • 2
  • Avigdor Gal
    • 1
  • Jan Mendling
    • 2
  • Avishai Mandelbaum
    • 1
  • Sarah Kadish
    • 3
  • Craig A. Bunnell
    • 3
  1. 1.Technion – Israel Institute of TechnologyHaifaIsrael
  2. 2.Vienna University of Economics and BusinessWienAustria
  3. 3.Dana-Farber Cancer InstituteBostonUSA

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