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Discovering Queues from Event Logs with Varying Levels of Information

  • Arik SenderovichEmail author
  • Sander J. J. Leemans
  • Shahar Harel
  • Avigdor Gal
  • Avishai Mandelbaum
  • Wil M. P. van der Aalst
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 256)

Abstract

Detecting and measuring resource queues is central to business process optimization. Queue mining techniques allow for the identification of bottlenecks and other process inefficiencies, based on event data. This work focuses on the discovery of resource queues. In particular, we investigate the impact of available information in an event log on the ability to accurately discover queue lengths, i.e. the number of cases waiting for an activity. Full queueing information, i.e. timestamps of enqueueing and exiting the queue, makes queue discovery trivial. However, often we see only the completions of activities. Therefore, we focus our analysis on logs with partial information, such as missing enqueueing times or missing both enqueueing and service start times. The proposed discovery algorithms handle concurrency and make use of statistical methods for discovering queues under this uncertainty. We evaluate the techniques using real-life event logs. A thorough analysis of the empirical results provides insights into the influence of information levels in the log on the accuracy of the measurements.

Keywords

Real-life Event Logs Measured Queue Length Case Wait Queue Mining Enqueue 
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.

References

  1. 1.
    van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  2. 2.
    Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A.: Queue mining for delay prediction in multi-class service processes. Technical report (2014)Google Scholar
  3. 3.
    Nakatumba, J.: Resource-aware business process management: analysis and support. Ph.D. thesis, Eindhoven University of Technology (2013)Google Scholar
  4. 4.
    Rogge-Solti, A., Mans, R.S., van der Aalst, W.M.P., Weske, M.: Repairing event logs using timed process models. In: Demey, Y.T., Panetto, H. (eds.) OTM 2013 Workshops 2013. LNCS, vol. 8186, pp. 705–708. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Buijs, J., van Dongen, B., van der Aalst, W.M.P.: A genetic algorithm for discovering process trees. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2012)Google Scholar
  7. 7.
    Adriansyah, A.: Aligning observed and modeled behavior. Ph.D. thesis, Eindhoven University of Technology (2014)Google Scholar
  8. 8.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc., New York (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
    Neuts, M.F.: Renewal processes of phase type. Nav. Res. Logistics Q. 25(3), 445–454 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Mandelbaum, A., Zeltyn, S.: Estimating characteristics of queueing networks using transactional data. Queueing systems 29(1), 75–127 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Mandelbaum, A., Zeltyn, S.: Service engineering in action: the Palm/Erlang-A queue, with applications to call centers. In: Advances in Services Innovations, pp. 17–45. Springer, Heidelberg (2007)Google Scholar
  12. 12.
    Kingman, J.: On queues in heavy traffic. J. Roy. Stat. Soc. Ser. B (Methodol.) 24, 383–392 (1962)Google Scholar
  13. 13.
    Asmussen, S.: Phase-type distributions and related point processes: fitting and recent advances. In: International Conference on Matrix-Analytic Methods in Stochastic Models, pp. 137–149 (1996)Google Scholar
  14. 14.
    Aslett, L.J., Wilson, S.P.: Markov chain monte carlo for inference on phasetype models. ISI (2011)Google Scholar
  15. 15.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013 Workshops. LNBIP, vol. 171, pp. 66–78. Springer, Heidelberg (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Arik Senderovich
    • 2
    Email author
  • Sander J. J. Leemans
    • 1
  • Shahar Harel
    • 2
  • Avigdor Gal
    • 2
  • Avishai Mandelbaum
    • 2
  • Wil M. P. van der Aalst
    • 1
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.TechnionHaifaIsrael

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