Advertisement

Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

  • Adriano AugustoEmail author
  • Raffaele Conforti
  • Marlon Dumas
  • Marcello La Rosa
  • Giorgio Bruno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9974)

Abstract

This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.

Keywords

Automated process discovery Process structuring BPMN 

Notes

Acknowledgments

This research is partly funded by the Australian Research Council (grant DP150103356) and the Estonian Research Council (grant IUT20-55).

References

  1. 1.
    Adriansyah, A., Munoz-Gama, J., Carmona, J., Dongen, B.F., Aalst, W.M.P.: Alignment based precision checking. In: Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 137–149. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36285-9_15 CrossRefGoogle Scholar
  2. 2.
    Adriansyah, A., van Dongen, B.F., van der Aalst, W.M.P.: Conformance checking using cost-based fitness analysis. In: Proceedings of EDOC. IEEE (2011)Google Scholar
  3. 3.
    Buijs, J.C.A.M., Dongen, B.F., Aalst, W.M.P.: On the role of fitness, precision, generalization and simplicity in process discovery. In: Meersman, R., Panetto, H., Dillon, T., Rinderle-Ma, S., Dadam, P., Zhou, X., Pearson, S., Ferscha, A., Bergamaschi, S., Cruz, I.F. (eds.) OTM 2012. LNCS, vol. 7565, pp. 305–322. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33606-5_19 CrossRefGoogle Scholar
  4. 4.
    Curran, T., Keller, G.: SAP R/3 Business Blueprint: Understanding the Business Process Reference Model. Prentice-Hall, Inc., Upper Saddle River (1997)Google Scholar
  5. 5.
    Dumas, M., García-Bañuelos, L., La Rosa, M., Uba, R.: Fast detection of exact clones in business process model repositories. Inf. Syst. 38(4), 619–633 (2013)CrossRefGoogle Scholar
  6. 6.
    Dumas, M., Rosa, M., Mendling, J., Mäesalu, R., Reijers, H.A., Semenenko, N.: Understanding business process models: the costs and benefits of structuredness. In: Ralyté, J., Franch, X., Brinkkemper, S., Wrycza, S. (eds.) CAiSE 2012. LNCS, vol. 7328, pp. 31–46. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-31095-9_3 CrossRefGoogle Scholar
  7. 7.
    Fahland, D., Favre, C., Koehler, J., Lohmann, N., Völzer, H., Wolf, K.: Analysis on demand: instantaneous soundness checking of industrial business process models. Data Knowl. Eng. 70(5), 448–466 (2011)CrossRefGoogle Scholar
  8. 8.
    Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)CrossRefGoogle Scholar
  9. 9.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of IJCAI, pp. 1137–1145. Morgan Kaufmann (1995)Google Scholar
  10. 10.
    Leemans, S.J.J., Fahland, D., Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38697-8_17 CrossRefGoogle Scholar
  11. 11.
    Mendling, J.: Metrics for Process Models: Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Molka, T., Redlich, D., Gilani, W., Zeng, X.-J., Drobek, M.: Evolutionary computation based discovery of hierarchical business process models. In: Abramowicz, W. (ed.) BIS 2015. LNBIP, vol. 208, pp. 191–204. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-19027-3_16 CrossRefGoogle Scholar
  13. 13.
    Oulsnam, G.: Unravelling unstructured programs. Comput. J. 25(3), 379–387 (1982)CrossRefzbMATHGoogle Scholar
  14. 14.
    Oulsnam, G.: The algorithmic transformation of schemas to structured form. Comput. J. 30(1), 43–51 (1987)CrossRefzbMATHGoogle Scholar
  15. 15.
    Polyvyanyy, A., García-Bañuelos, L., Dumas, M.: Structuring acyclic process models. Inf. Syst. 37(6), 518–538 (2012)CrossRefGoogle Scholar
  16. 16.
    Polyvyanyy, A., García-Bañuelos, L., Fahland, D., Weske, M.: Maximal structuring of acyclic process models. Comput. J. 57(1), 12–35 (2014)CrossRefGoogle Scholar
  17. 17.
    Polyvyanyy, A., Vanhatalo, J., Völzer, H.: Simplified computation and generalization of the refined process structure tree. In: Bravetti, M., Bultan, T. (eds.) WS-FM 2010. LNCS, vol. 6551, pp. 25–41. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-19589-1_2 CrossRefGoogle Scholar
  18. 18.
    van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)zbMATHGoogle Scholar
  19. 19.
    van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  20. 20.
    De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B.: A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf. Syst. 37(7), 654–676 (2012)CrossRefGoogle Scholar
  21. 21.
    Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (FHM). In: Proceedings of CIDM. IEEE (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Adriano Augusto
    • 1
    • 2
    Email author
  • Raffaele Conforti
    • 1
  • Marlon Dumas
    • 3
  • Marcello La Rosa
    • 1
  • Giorgio Bruno
    • 2
  1. 1.Queensland University of TechnologyBrisbaneAustralia
  2. 2.Politecnico di TorinoTurinItaly
  3. 3.University of TartuTartuEstonia

Personalised recommendations