Using Process Mining to Generate Accurate and Interactive Business Process Maps

  • W. M. P. van der Aalst
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 37)

Abstract

The quality of today’s digital maps is very high. This allows for new functionality as illustrated by modern car navigation systems (e.g., TomTom, Garmin, etc.), Google maps, Google Street View, Mashups using geo-tagging (e.g., Panoramio, HousingMaps, etc.), etc. People can seamlessly zoom in and out using the interactive maps in such systems. Moreover, all kinds of information can be projected on these interactive maps (e.g., traffic jams, four-bedroom apartments for sale, etc.). Process models can be seen as the “maps” describing the operational processes of organizations. Unfortunately, accurate and interactive process maps are typically missing when it comes to business process management. Either there are no good maps or the maps are static or outdated. Therefore, we propose to automatically generate business process maps using process mining techniques. By doing this, there is a close connection between these maps and the actual behavior recorded in event logs. This will allow for high-quality process models showing what really happened. Moreover, this will also allow for the projection of dynamic information, e.g., the “traffic jams” in business processes. In fact, the combination of accurate maps, historic information, and information about current process instances, allows for prediction and recommendation. For example, just like TomTom can predict the arrival time at a particular location, process mining techniques can be used to predict when a process instance will finish.

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References

  1. 1.
    van der Aalst, W.M.P.: Business Process Management Demystified: A Tutorial on Models, Systems and Standards for Workflow Management. In: Desel, J., Reisig, W., Rozenberg, G. (eds.) Lectures on Concurrency and Petri Nets. LNCS, vol. 3098, pp. 1–65. Springer, Heidelberg (2004)Google Scholar
  2. 2.
    van der Aalst, W.M.P.: TomTom for Business Process Management (TomTom4BPM). In: Gordijn, J. (ed.) Proceedings of the 21st International Conference on Advanced Information Systems Engineering (CAiSE 2009). LNCS. Springer, Heidelberg (2009)Google Scholar
  3. 3.
    van der Aalst, W.M.P., van Dongen, B.F., Günther, C.W., Mans, R.S., Alves de Medeiros, A.K., Rozinat, A., Rubin, V., Song, M., Verbeek, H.M.W., Weijters, A.J.M.M.: 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)CrossRefGoogle Scholar
  4. 4.
    van der Aalst, W.M.P., ter Hofstede, A.H.M.: YAWL: Yet Another Workflow Language. Information Systems 30(4), 245–275 (2005)CrossRefGoogle Scholar
  5. 5.
    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), 713–732 (2007)CrossRefGoogle Scholar
  6. 6.
    van der Aalst, W.M.P., Rubin, V., van Dongen, B.F., Kindler, E., Günther, C.W.: Process Mining: A Two-Step Approach to Balance Between Underfitting and Overfitting. Software and Systems Modeling (2009)Google Scholar
  7. 7.
    van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time Prediction Based on Process Mining. BPM Center Report BPM-09-04, BPMcenter.org (2009)Google Scholar
  8. 8.
    van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow Mining: A Survey of Issues and Approaches. Data and Knowledge Engineering 47(2), 237–267 (2003)CrossRefGoogle Scholar
  9. 9.
    van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  10. 10.
    Agrawal, R., Gunopulos, D., Leymann, F.: Mining Process Models from Workflow Logs. In: Sixth International Conference on Extending Database Technology, pp. 469–483 (1998)Google Scholar
  11. 11.
    Cook, J.E., Wolf, A.L.: Discovering Models of Software Processes from Event-Based Data. ACM Transactions on Software Engineering and Methodology 7(3), 215–249 (1998)CrossRefGoogle Scholar
  12. 12.
    Crooy, R.: Predictions in Information Systems: A process mining perspective. Master’s thesis, Eindhoven University of Technology, Eindhoven (2008)Google Scholar
  13. 13.
    Datta, A.: Automating the Discovery of As-Is Business Process Models: Probabilistic and Algorithmic Approaches. Information Systems Research 9(3), 275–301 (1998)CrossRefGoogle Scholar
  14. 14.
    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.) CoopIS 2008, OTM 2008, Part I. LNCS, vol. 5331, pp. 319–336. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    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 & Sons, Chichester (2005)Google Scholar
  16. 16.
    Günther, C.W., van der Aalst, W.M.P.: Fuzzy Mining: Adaptive Process Simplification Based on Multi-perspective Metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Herbst, J.: A Machine Learning Approach to Workflow Management. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS, vol. 1810, pp. 183–194. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  18. 18.
    de Leoni, M., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Visual Support for Work Assignment in Process-Aware Information Systems. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 67–83. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Alves de Medeiros, A.K., Weijters, A.J.M.M., van der Aalst, W.M.P.: Genetic Process Mining: An Experimental Evaluation. Data Mining and Knowledge Discovery 14(2), 245–304 (2007)CrossRefGoogle Scholar
  20. 20.
    Rozinat, A., Mans, R.S., Song, M., van der Aalst, W.M.P.: Discovering Simulation Models. Information Systems 34(3), 305–327 (2009)Google Scholar
  21. 21.
    Rozinat, A., Wynn, M.T., van der Aalst, W.M.P., ter Hofstede, A.H.M., Fidge, C.: Workflow Simulation for Operational Decision Support Using Design, Historic and State Information. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 196–211. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  22. 22.
    Schellekens, B.: Cycle Time Prediction in Staffware. Master’s thesis, Eindhoven University of Technology, Eindhoven (2009)Google Scholar
  23. 23.
    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)CrossRefGoogle Scholar
  24. 24.
    Staffware. Staffware Process Suite Version 2 – White Paper. Staffware PLC, Maidenhead, UK (2003)Google Scholar
  25. 25.
    Verwer, S.E., de Weerdt, M.M., Witteveen, C.: Efficiently learning timed models from observations. In: Wehenkel, L., Geurts, P., Maree, R. (eds.) Benelearn, Benelearn, pp. 75–76. University of Liege (2008)Google Scholar
  26. 26.
    Weber, B., Wild, W., Breu, R.: CBRFlow: Enabling Adaptive Workflow Management Through Conversational Case-Based Reasoning. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS, vol. 3155, pp. 434–448. Springer, Heidelberg (2004)Google Scholar
  27. 27.
    Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering Workflow Models from Event-Based Data using Little Thumb. Integrated Computer-Aided Engineering 10(2), 151–162 (2003)Google Scholar
  28. 28.
    van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.A.J., Serebrenik, A.: Process Discovery using Integer Linear Programming. In: van Hee, K.M., Valk, R. (eds.) PETRI NETS 2008. LNCS, vol. 5062, pp. 368–387. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  29. 29.
    Weske, M.: Business Process Management: Concepts, Languages, Architectures. Springer, Berlin (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • W. M. P. van der Aalst
    • 1
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands

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