Advertisement

Discovery and Validation of Queueing Networks in Scheduled Processes

  • Arik Senderovich
  • Matthias WeidlichEmail author
  • Avigdor Gal
  • Avishai Mandelbaum
  • Sarah Kadish
  • Craig A. Bunnell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9097)

Abstract

Service processes, for example in transportation, telecommunications or the health sector, are the backbone of today’s economies. Conceptual models of such service processes enable operational analysis that supports, e.g., resource provisioning or delay prediction. Automatic mining of such operational models becomes feasible in the presence of event-data traces. In this work, we target the mining of models that assume a resource-driven perspective and focus on queueing effects. We propose a solution for the discovery and validation problem of scheduled service processes - processes with a predefined schedule for the execution of activities. Our prime example for such processes are complex outpatient treatments that follow prior appointments. Given a process schedule and data recorded during process execution, we show how to discover Fork/Join networks, a specific class of queueing networks, and how to assess their operational validity. We evaluate our approach with a real-world dataset comprising clinical pathways of outpatient clinics, recorded by a real-time location system (RTLS). We demonstrate the value of the approach by identifying and explaining operational bottlenecks.

Keywords

Service Time Service Process Service Policy Schedule Process Process Execution 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Daskin, M.S.: Service Science. Wiley. com (2011)Google Scholar
  3. 3.
    Froehle, C.M., Magazine, M.J.: Improving scheduling and flow in complex outpatient clinics. In: Handbook of Healthcare Operations Management, pp. 229–250. Springer (2013)Google Scholar
  4. 4.
    Gal, A., Mandelbaum, A., Schnitzler, F., Senderovich, A., Weidlich, M.: Traveling Time Prediction in Scheduled Transportation with Journey Segments. Tech. Rep., Technion (2014)Google Scholar
  5. 5.
    Ammar, M.H., Gershwin, S.B.: Equivalence relations in queueing models of fork/join networks with blocking. Performance Evaluation 10(3), 233–245 (1989)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Mandelbaum, A.: Service engineering (science, management): A subjective view. Technical report, Technion-Israel Institute of Technology (2007)Google Scholar
  7. 7.
    Bolch, G., Greiner, S., de Meer, H., Trivedi, K.S.: Queueing Networks and Markov Chains - Modeling and Performance Evaluation with Computer Science Applications. Wiley (2006)Google Scholar
  8. 8.
    Kendall, D.G.: Stochastic Processes Occurring in the Theory of Queues and their Analysis by the Method of the Imbedded Markov Chain. The Annals of Mathematical Statistics 24(3), 338–354 (1953)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    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) CrossRefGoogle Scholar
  10. 10.
    Sargent, R.G.: Verification and validation of simulation models. In: WSC, pp. 183–198 (2011)Google Scholar
  11. 11.
    Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: Quality dimensions in process discovery: The importance of fitness, precision, generalization and simplicity. Int. J. Cooperative Inf. Syst. 23(1) (2014)Google Scholar
  12. 12.
    Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)CrossRefGoogle Scholar
  13. 13.
    Bose, R.P.J.C., van der Aalst, W.M.P.: Process diagnostics using trace alignment: Opportunities, issues, and challenges. Inf. Syst. 37(2), 117–141 (2012)CrossRefGoogle Scholar
  14. 14.
    Weidlich, M., Polyvyanyy, A., Desai, N., Mendling, J., Weske, M.: Process compliance analysis based on behavioural profiles. Inf. Syst. 36(7), 1009–1025 (2011)CrossRefGoogle Scholar
  15. 15.
    vanden Broucke, S.K.L.M., Weerdt, J.D., Vanthienen, J., Baesens, B.: Determining process model precision and generalization with weighted artificial negative events. IEEE Trans. Knowl. Data Eng. 26(8), 1877–1889 (2014)CrossRefGoogle Scholar
  16. 16.
    Dallery, Y., Liu, Z., Towsley, D.: Equivalence, reversibility, symmetry and concavity properties in fork-join queuing networks with blocking. J. ACM 41(5), 903–942 (1994)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Rozinat, A., Mans, R., Song, M., van der Aalst, W.M.P.: Discovering simulation models. Information Systems 34(3), 305–327 (2009)CrossRefGoogle Scholar
  18. 18.
    van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  19. 19.
    Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A.: Mining resource scheduling protocols. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 200–216. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  20. 20.
    Brown, L., Gans, N., Mandelbaum, A., Sakov, A., Shen, H., Zeltyn, S., Zhao, L.: Statistical Analysis of a Telephone Call Center. Journal of the American Statistical Association 100(469), 36–50 (2005)CrossRefzbMATHMathSciNetGoogle Scholar
  21. 21.
    Zhang, P., Serban, N.: Discovery, visualization and performance analysis of enterprise workflow. Computational statistics & data analysis 51(5), 2670–2687 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  22. 22.
    Bickel, P., Doksum, K.: Mathematical Statistics: Basic Ideas and Selected Topics. Holden-Day series in probability and statistics, vol. 1. Prentice Hall (2001)Google Scholar
  23. 23.
    Pinedo, M.: Planning and scheduling in manufacturing and services. Springer (2005)Google Scholar
  24. 24.
    Buzacott, J.A., Shanthikumar, J.G.: Stochastic Models of Manufacturing Systems. Prentice Hall, Englewood Cliffs, NJ (1993)zbMATHGoogle Scholar
  25. 25.
    Mans, R.S., Russell, N.C., van der Aalst, W.M.P., Moleman, A.J., Bakker, P.J.M.: Schedule-aware workflow management systems. In: Jensen, K., Donatelli, S., Koutny, M. (eds.) Transactions on Petri Nets and Other Models of Concurrency IV. LNCS, vol. 6550, pp. 121–143. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  26. 26.
    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
  27. 27.
    van der Aalst, W.M.P., Schonenberg, M., Song, M.: Time prediction based on process mining. Information Systems 36(2), 450–475 (2011)CrossRefGoogle Scholar
  28. 28.
    Rogge-Solti, A., Weske, M.: Prediction of remaining service execution time using stochastic petri nets with arbitrary firing delays. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 389–403. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  29. 29.
    Pika, A., Wynn, M.T., Fidge, C.J., ter Hofstede, A.H.M., Leyer, M., van der Aalst, W.M.P.: An extensible framework for analysing resource behaviour using event logs. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 564–579. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  30. 30.
    Nakatumba, J., van der Aalst, W.M.P.: Analyzing resource behavior using process mining. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 69–80. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  31. 31.
    Van der Aalst, W.: Petri net based scheduling. Operations-Res.-Spektr. 18(4), 219–229 (1996)CrossRefzbMATHGoogle Scholar
  32. 32.
    Taghiabadi, E.R., Gromov, V., Fahland, D., van der Aalst, W.M.P.: Compliance checking of data-aware and resource-aware compliance requirements. In: Meersman, R., Panetto, H., Dillon, T., Missikoff, M., Liu, L., Pastor, O., Cuzzocrea, A., Sellis, T. (eds.) OTM 2014. LNCS, vol. 8841, pp. 237–257. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  33. 33.
    de Leoni, M., van der Aalst, W.M.P., van Dongen, B.F.: Data- and resource-aware conformance checking of business processes. In: Abramowicz, W., Kriksciuniene, D., Sakalauskas, V. (eds.) BIS 2012. LNBIP, vol. 117, pp. 48–59. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  34. 34.
    Adriansyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Conformance checking using cost-based fitness analysis. In: EDOC, IEEE Computer Society, pp. 55–64 (2011)Google Scholar
  35. 35.
    Atar, R., Mandelbaum, A., Zviran, A.: Control of fork-join networks in heavy traffic. In: 50th Annual Allerton Conference on Communication, Control, and Computing, pp. 823–830 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Arik Senderovich
    • 1
  • Matthias Weidlich
    • 2
    Email author
  • Avigdor Gal
    • 1
  • Avishai Mandelbaum
    • 1
  • Sarah Kadish
    • 3
  • Craig A. Bunnell
    • 3
  1. 1.Technion – Israel Institute of TechnologyHaifaIsrael
  2. 2.Imperial College LondonLondonUK
  3. 3.Dana-Farber Cancer InstituteBostonUSA

Personalised recommendations