End-to-End Process Extraction in Process Unaware Systems

  • Sukriti Goel
  • Jyoti M. Bhat
  • Barbara Weber
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 132)


Knowledge of current business processes is a critical requirement for organizational initiatives like compliance management, regulatory reporting, process optimization, reengineering the IT systems and outsourcing. Existing process discovery techniques expect process execution information or event logs while organization’s business processes are often executed on heterogeneous systems across different departments, by integration and data hand-offs between systems. Traditional information systems, however, are designed for storing and processing transaction data which persists in databases and other data storage mechanisms. In this paper we identify the challenges and propose a solution for extracting end-to-end processes from persistent process execution data available in multiple heterogeneous applications. The approach consists of analyzing persistent system data to identify and obtain events in a non-intrusive manner. The approach to get the end-to-end process involves a combination of data and process mining.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Leymann, F., Reisig, W., Thatte, S.R., van der Aalst, W.M.P.: The Role of Business Processes in Service Oriented Architectures, number 6291, Dagstuhl Seminar Proceedings. Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany (July 2006)Google Scholar
  2. 2.
    Verner, L.: The Challenge of Process Discovery, BP Trends (May 2004)Google Scholar
  3. 3.
    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
  4. 4.
    Blickle, T., Hess, H.: Automatic Process Discovery with ARIS Process Performance Manager (ARIS PPM), Expert Paper, IDS ScheerGoogle Scholar
  5. 5.
    Rozinat, A., van der Aalst, W.M.P.: Conformance Checking of Processes Based on Monitoring Real Behavior. Information Systems 33(1), 64–95 (2008)CrossRefGoogle Scholar
  6. 6.
    van der Aalst, W.M.P., Weijters, A., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  7. 7.
    Woodfill, J., Stonebraker, M.: An Implementation of Hypothetical Relations. In: Schkolnick, M., Thanos, C. (eds.) 9th International Conference on Very Large Data Bases Very Large Data Bases, pp. 157–166. Morgan Kaufmann Publishers, San Francisco (1983)Google Scholar
  8. 8.
    Dumas, M., van der Aalst, W.M.P., Hofstede, T.: Process-aware information systems: Bridging people and software through process technology. John Wiley & Sons, Inc. (2005)Google Scholar
  9. 9.
    Curbera, F., Doganata, Y., Martens, A., Mukhi, N.K., Slominski, A.: Business Provenance – A Technology to Increase Traceability of End-to-End Operations. In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part I. LNCS, vol. 5331, pp. 100–119. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Motahari-Nezhad, H.R., Saint-Paul, R., Benatallah, B., Casati, F., Andritsos, P.: Process Spaceship: Discovering Process views in Process Spaces, Technical Report, UNSW-CSE-TR-0721, The School of Computer Science and Engineering, Australia (December 2007)Google Scholar
  12. 12.
    Alves, A.K.: Using Genetic Algorithms to Mine Process Models: Representation, Operators and Results (2003)Google Scholar
  13. 13.
    van Dongen, B.F., van der Aalst, W.M.P.: Multi-phase Process Mining: Building Instance Graphs. In: Atzeni, P., Chu, W., Lu, H., Zhou, S., Ling, T.-W. (eds.) ER 2004. LNCS, vol. 3288, pp. 362–376. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Agrawal, R., Gunopulos, D., Leymann, F.: Mining Process Models from Workflow Logs. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 469–483. Springer, Heidelberg (1998)Google Scholar
  15. 15.
    Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust Process Discovery with Artificial Negative Events. The Journal of Machine Learning Research 10 (December 2009)Google Scholar
  16. 16.
    Basili, V.R., Weiss, D.M.: A methodology for collecting valid software engineering data. IEEE Transactions on Software Engineering, SE-10(6), 728–738 (1984)CrossRefGoogle Scholar
  17. 17.
    Wolf, A.L., Rosenblum, D.S.: A Study in Software Process Capture and Analysis. In: 2nd International Conference on the Software Process, Berlin, Germany (February 1993)Google Scholar
  18. 18.
    van der Aalst, W.M.P.: Process Mining and Monitoring Processes and Services: Workshop Report. In: Leymann, F., Reisig, W., Thatte, S.R., van der Aalst, W.M.P. (eds.) The Role of Business Processes in Service Oriented Architectures. Dagstuhl Seminar Proceedings, vol. 6291, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany (July 2006)Google Scholar
  19. 19.
    Castellanos, M., Alves de Medeiros, K., Mendling, J., Weber, B., Weitjers, A.J.M.M.: Business Process Intelligence. In: Cardoso, J., van der Aalst, W.M.P. (eds.) Handbook of Research on Business Process Modeling, pp. 456–480. Idea Group Inc. (2009)Google Scholar
  20. 20.
    Pérez-Castillo, R., Weber, B., de Guzmán, I.G.R., Piattini, M.: Process mining through dynamic analysis for modernising legacy systems. IET Software 5(3), 304–319 (2011)CrossRefGoogle Scholar
  21. 21.
    Pérez-Castillo, R., Weber, B., Pinggera, J., Zugal, S., de Guzmán, I.G.R., Piattini, M.: Generating event logs from non-process-aware systems enabling business process mining. Enterprise IS 5(3), 301–335 (2011)CrossRefGoogle Scholar
  22. 22.
    Ferreira, D.R., Gillblad, D.: Discovering Process Models from Unlabelled Event Logs. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 143–158. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  23. 23.
    Burattin, A., Vigo, R.: A framework for semi-automated process instance discovery from decorative attributes. In: CIDM, pp. 176–183 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sukriti Goel
    • 1
  • Jyoti M. Bhat
    • 2
  • Barbara Weber
    • 3
  1. 1.BPM Research GroupInfosys Labs, Infosys LimitedIndia
  2. 2.Information SystemsIndian Institute of ManagementBangaloreIndia
  3. 3.Department of Computer ScienceUniversity of InnsbruckInnsbruckAustria

Personalised recommendations