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Process Remaining Time Prediction Using Query Catalogs

  • Alfredo BoltEmail author
  • Marcos Sepúlveda
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 171)

Abstract

A relevant topic in business process management is the ability to predict the outcome of a process in order to establish a priori recommendations about how to go forward from a certain point in the process. Recommendations are made based on different predictions, like the process remaining time or the process cost. Predicting remaining time is an issue that has been addressed by few authors, whose approaches have limitations inherent to their designs. This article presents a novel approach for predicting process remaining time based on query catalogs that store the information of process events in the form of partial trace tails, which are then used to estimate the remaining time of new executions of the process, ensuring greater accuracy, flexibility and dynamism that the best methods currently available. This was tested in both simulated and real process event logs. The methods defined in this article may be incorporated into recommendation systems to give a better estimation of process remaining time, allowing them to dynamically learn with each new trace passing through the system.

Keywords

Process mining Process remaining time prediction Query catalogs 

References

  1. 1.
    Dumas, M., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Process-Aware Information Systems: Bridging People and Software Through Process Technology. Wiley, New York (2005)CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P., Reijers, H.A., Weijters, A.J.M.M., van Dongen, B.F., de Medeiros, A.K.A., Song, M., Verbeek, H.M.W.: Business process mining: an industrial application. Inf. Syst. 32(5), 713–732 (2007)CrossRefGoogle Scholar
  3. 3.
    Schonenberg, H., Weber, B., van Dongen, B.F., van der Aalst, W.M.P.: Supporting flexible processes through recommendations based on history. In: Dumas, M., Reichert, M., Shan, M.C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 51–66. Springer, Heidelberg (2008)Google Scholar
  4. 4.
    van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)CrossRefGoogle Scholar
  5. 5.
    van Dongen, B.F., Crooy, R.A., van der Aalst, W.M.P.: Cycle time prediction: when will this case finally be finished? In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part I. LNCS, vol. 5331, pp. 319–336. Springer, Heidelberg (2008)Google Scholar
  6. 6.
    Backus, P., Janakiram, M., Mowzoon, S., Runger, G.C., Bhargava, A.: Factory cycle time prediction with a data-mining approach. IEEE Trans. Semicond. Manuf. 19(2), 252–258 (2006)CrossRefGoogle Scholar
  7. 7.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp. 1137–1143 (1995)Google Scholar
  8. 8.
    Burattin, A., Sperduti, A.: PLG: a framework for the generation of business process models and their execution logs. In: Proceedings of the 6th International Workshop on Business Process Intelligence (BPI 2010), pp. 214–219. Springer, HeidelbergGoogle Scholar
  9. 9.
    Peterson, J.L.: Petri Nets. ACM Comput. Surv. (CSUR) 9(3), 223–252 (1977)CrossRefzbMATHGoogle Scholar
  10. 10.
    van der Aalst, W.M.P., et al.: ProM 4.0: comprehensive support for real process analysis. In: Kleijn, J., Yakovlev, A. (eds.) ICATPN 2007. LNCS, vol. 4546, pp. 484–494. Springer, Heidelberg (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of EngineeringUniversidad Finis TerraeSantiagoChile
  2. 2.Computer Science Department, School of EngineeringPontificia Universidad Católica de ChileSantiagoChile

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