Cycle Time Prediction: When Will This Case Finally Be Finished?

  • B. F. van Dongen
  • R. A. Crooy
  • W. M. P. van der Aalst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5331)


A typical question for people dealing with administrative processes is: “When will my case be finished?”. In this paper, we show how this question can be answered, using historic information in the form of event logs of the systems supporting these administrative processes. Many information systems record information about activities performed for past cases in logs. Hence, to provide insights into the remaining cycle time of a case, the current case can be compared to all past ones.

The most trivial way of estimating the remaining cycle time of a case is by looking at the average cycle time and deducting the already past time of the case under consideration. However, in this paper we show how to compute the remaining cycle time using non-parametric regression on the data recorded in event logs. An experiment is presented that demonstrates that our techniques perform well on logs taken from practice.


Kernel Function Cycle Time Target Variable Activity Duration Partial Case 
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  1. 1.
    van der Aalst, W.M.P., van Dongen, B.F., Günther, C.W., et al.: ProM 4.0: Comprehensive Supports for Real Process Analysis. In: Kleijn, J., Yakovlev, A. (eds.) ICATPN 2007. LNCS, vol. 4546. Springer, Heidelberg (2007)Google Scholar
  2. 2.
    van der Aalst, W.M.P., Reijers, H.A., Weijters, A.J.M.M., van Dongen, B.F., Alves de Medeiros, A.K., Song, M., Verbeek, H.M.W.: Business Process Mining: An Industrial Application. Information Systems 32(5), 713 (2007)CrossRefGoogle Scholar
  3. 3.
    Aitchison, J., Aitken, C.: Multivariate binary discrimination by the kernel method. Biometrika 63(3), 413–420 (1976)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Crooy, R.A.: Predictions in Information Systems, a process mining perspective. Master Thesis, Eindhoven University of Technology (2008); via Digital Library of Eindhoven University of Technology (November 2008) (to appear)Google Scholar
  5. 5.
    Dippon, J., Fritz, P., Kohler, M.: A statistical approach to case based reasoning, with application to breast cancer data. Computational Statistics and Data Analysis 40(3), 579–602 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Hardle, W.: Applied Nonparametric Regression. Cambridge University Press, Cambridge (1990)CrossRefzbMATHGoogle Scholar
  7. 7.
    de Medeiros, A.K.A.: Genetic Process Mining. PhD thesis, Eindhoven University of Technology, Eindhoven (2006)Google Scholar
  8. 8.
    R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008), ISBN 3-900051-07-0Google Scholar
  9. 9.
    Racine, J., Li, Q.: Nonparametric estimation of regression functions with both categorical and continuous data. Journal of Econometrics 119(1), 99–130 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Urbanek, S.: Rserve - A Fast Way to Provide R Functionality to Applications. In: Hornik, K., Leisch, F., Zeileis, A. (eds.) Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003) (2003)Google Scholar
  11. 11.
    van der Aalst, W.M.P., Dumas, M., Ouyang, C., Rozinat, A., Verbeek, E.: Conformance checking of service behavior. ACM Trans. Interet Technol. 8(3), 1–30 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • B. F. van Dongen
    • 1
  • R. A. Crooy
    • 1
  • W. M. P. van der Aalst
    • 1
  1. 1.Department of Mathematics and Computer ScienceTechnische Universiteit EindhovenEindhovenThe Netherlands

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