Advertisement

Abstract

Enterprise systems implementations are often accompanied by changes in the business processes of the organizations in which they take place. However, not all the changes are desirable. In “vanilla” implementations it is possible that the newly operational business process requires many additional steps as “workarounds” of the system limitations, and is hence performed in an inefficient manner. Such inefficiencies are reflected in the event log of the system as recurring patterns of log entries. Once identified, they can be resolved over time by modifications to the enterprise system. Addressing this situation, the paper proposes an approach for identifying inefficient workarounds by mining the related patterns in an event log. The paper characterizes such patterns, proposes a mining algorithm, and rules for prioritizing the required process improvements.

Keywords

Process mining Enterprise systems Event log 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    van der Aalst, W.M.P.: Business alignment: using process mining as a tool for Delta analysis and conformance testing. Requirements Engineering Journal 10(3), 198–211 (2005)CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P., de Medeiros, A.K.A.: Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance. In: Busi, N., Gorrieri, R., Martinelli, F. (eds.) Second International Workshop on Security Issues with Petri Nets and other Computational Models (WISP 2004). STAR, Servizio Tipografico Area della Ricerca, CNR Pisa, Italy, pp. 69–84 (2004)Google Scholar
  3. 3.
    van der Aalst, W.M.P., van Dongen, B.F.: Discovering Workflow Performance Models from Timed Logs. In: Han, Y., Tai, S., Wikarski, D. (eds.) EDCIS 2002. LNCS, vol. 2480, pp. 45–63. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    van der Aalst, W.M.P., ter Hofstede, A.H.M., Kiepuszewski, B., Barros, A.P.: Workflow Patterns. Distributed and Parallel Databases 14(1), 5–51 (2003)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., Reijers, H.A., Song, M.: Discovering Social Networks from Event Logs. Computer Supported Cooperative Work 14(6), 549–593 (2005)CrossRefGoogle Scholar
  7. 7.
    Alves de Medeiros, A.K., Guzzo, A., Greco, G., van der Aalst, W.M.P., Weijters, A.J.M.M., van Dongen, B., Saccà, D.: Process Mining Based on Clustering: A Quest for Precision. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 17–29. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Bandinelli, S., Fuggetta, A., Lavazza, L., Loi, M., Picco, G.: Modeling and improving an industrial software process. IEEE Trans. Softw. Eng. 21(5), 440–454 (1995)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Davenport, T.: Putting the Enterprise into the Enterprise System. Harvard Business Review 76(4), 121–131 (1998)Google Scholar
  11. 11.
    van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The ProM framework: A new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Mans, R.S., Schonenberg, M.H., Song, M., van der Aalst, W.M.P., Bakker, P.J.M.: Process Mining in Health Care. In: Azevedo, L., Londral, A.R. (eds.) International Conference on Health Informatics (HEALTHINF 2008), Funchal, Maldeira, Portugal, January 28-31, 2008, pp. 118–125 (2008)Google Scholar
  13. 13.
    Maruster, L., van der Aalst, W.M.P., Weijters, A.J.M.M., van den Bosch, A., Daelemans, W.: Automated Discovery of Workflow Models from Hospital Data. In: Kröse, B., de Rijke, M., Schreiber, G., van Someren, M. (eds.) Proceedings of the 13th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 2001), pp. 183–190 (2001)Google Scholar
  14. 14.
    Maruster, L., Wortmann, J.C., Weijters, A.J.M.M., van der Aalst, W.M.P.: Discovering Distributed Processes in Supply Chains. In: Proceedings of the International Conference on Advanced Production Management Systems (APMS 2002), pp. 119–128 (2002)Google Scholar
  15. 15.
    Parr, A.N., Shanks, G.: A taxonomy of ERP implementation approaches. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, vol. 1, pp. 1–10. IEEE Press, Los Alamitos (2000)CrossRefGoogle Scholar
  16. 16.
    Rubin, V., Günther, C.W., van der Aalst, W.M.P., Kindler, E., van Dongen, B.F., Schäfer, W.: Process Mining Framework for Software Processes. In: Wang, Q., Pfahl, D., Raffo, D.M. (eds.) ICSP 2007. LNCS, vol. 4470, pp. 169–181. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace Clustering in Process Mining. In: 4th Workshop on Business Process Intelligence (BPI 2008) (2008)Google Scholar
  18. 18.
    Weijters, A.J.M.M., van der Aalst, W.M.P.: Process mining: discovering workflow models from event-based data. In: Kröse, B., de Rijke, M., Schreiber, G., van Someren, M. (eds.) Proceedings of the 13th Belgium–Netherlands Conference on Artificial Intelligence (BNAIC 2001), pp. 283–290 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dolev Mezebovsky
    • 1
  • Pnina Soffer
    • 1
  • Ilan Shimshoni
    • 1
  1. 1.University of Haifa, Carmel MountainHaifaIsrael

Personalised recommendations