Discovering Block-Structured Process Models from Incomplete Event Logs

  • Sander J. J. Leemans
  • Dirk Fahland
  • Wil M. P. van der Aalst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8489)


One of the main challenges in process mining is to discover a process model describing observed behaviour in the best possible manner. Since event logs only contain example behaviour and one cannot assume to have seen all possible process executions, process discovery techniques need to be able to handle incompleteness. In this paper, we study the effects of such incomplete logs on process discovery. We analyse the impact of incompleteness of logs on behavioural relations, which are abstractions often used by process discovery techniques. We introduce probabilistic behavioural relations that are less sensitive to incompleteness, and exploit these relations to provide a more robust process discovery algorithm. We prove this algorithm to be able to rediscover a model of the original system. Furthermore, we show in experiments that our approach even rediscovers models from incomplete event logs that are much smaller than required by other process discovery algorithms.


process discovery block-structured process models rediscoverability process trees 


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  1. 1.
    van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer (2011)Google Scholar
  2. 2.
    van der Aalst, W., Buijs, J., van Dongen, B.: Towards improving the representational bias of process mining. In: Aberer, K., Damiani, E., Dillon, T. (eds.) SIMPDA 2011. LNBIP, vol. 116, pp. 39–54. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    van der Aalst, W., Weijters, A., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  4. 4.
    Badouel, E., Darondeau, P.: Theory of Regions. In: Reisig, W., Rozenberg, G. (eds.) APN 1998. LNCS, vol. 1491, pp. 529–586. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Badouel, E.: On the α-reconstructibility of workflow nets. In: Haddad, S., Pomello, L. (eds.) PETRI NETS 2012. LNCS, vol. 7347, pp. 128–147. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Bergenthum, R., Desel, J., Mauser, S., Lorenz, R.: Synthesis of Petri nets from term based representations of infinite partial languages. Fundam. Inform. 95(1), 187–217 (2009)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Bloom, S.L., Ésik, Z.: Free shuffle algebras in language varieties. Theor. Comput. Sci. 163(1&2), 55–98 (1996)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Buijs, J., van Dongen, B., van der Aalst, W.: A genetic algorithm for discovering process trees. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2012)Google Scholar
  9. 9.
    Carmona, J.: Projection approaches to process mining using region-based techniques. Data Mining and Knowledge Discovery 24(1), 218–246 (2012)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Cortadella, J., Kishinevsky, M., Lavagno, L., Yakovlev, A.: Deriving Petri nets for finite transition systems. IEEE Trans. Computers 47(8), 859–882 (1998)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Darondeau, P.: Region based synthesis of P/T-nets and its potential applications. In: Nielsen, M., Simpson, D. (eds.) ICATPN 2000. LNCS, vol. 1825, pp. 16–23. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Darondeau, P.: Unbounded Petri net synthesis. In: Desel, J., Reisig, W., Rozenberg, G. (eds.) Lectures on Concurrency and Petri Nets. LNCS, vol. 3098, pp. 413–438. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    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. Information Systems 37, 654–676 (2012)CrossRefGoogle Scholar
  14. 14.
    van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M.T., 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
  15. 15.
    Ehrenfeucht, A., Rozenberg, G.: Partial (set) 2-structures. Acta Informatica 27(4), 343–368 (1990)MathSciNetCrossRefGoogle 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.
    Leemans, S.J.J., Fahland, D., van der 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)CrossRefGoogle Scholar
  18. 18.
    Leemans, S., Fahland, D., van der Aalst, W.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Business Process Management Workshops. Springer (2013)Google Scholar
  19. 19.
    Leemans, S., Fahland, D., van der Aalst, W.: Discovering block-structured process models from incomplete event logs. Tech. Rep. BPM-14-05, Eindhoven University of Technology (March 2014)Google Scholar
  20. 20.
    Linz, P.: An introduction to formal languages and automata. Jones & Bartlett Learning (2011)Google Scholar
  21. 21.
    Lorenz, R., Mauser, S., Juhás, G.: How to synthesize nets from languages: a survey. In: Winter Simulation Conference, WSC, pp. 637–647 (2007)Google Scholar
  22. 22.
    Polyvyanyy, A., Vanhatalo, J., Völzer, H.: Simplified computation and generalization of the refined process structure tree. In: Bravetti, M. (ed.) WS-FM 2010. LNCS, vol. 6551, pp. 25–41. Springer, Heidelberg (2011)Google Scholar
  23. 23.
    Reisig, W., Schnupp, P., Muchnick, S.: Primer in Petri Net Design. Springer (1992)Google Scholar
  24. 24.
    Rozinat, A., de Medeiros, A.K.A., Günther, C.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The need for a process mining evaluation framework in research and practice. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM 2007 Workshops. LNCS, vol. 4928, pp. 84–89. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  25. 25.
    Rozinat, A., Veloso, M., van der Aalst, W.: Evaluating the quality of discovered process models. In: 2nd Int. Workshop on the Induction of Process Models, pp. 45–52 (2008)Google Scholar
  26. 26.
    Schimm, G.: Generic linear business process modeling. In: Mayr, H.C., Liddle, S.W., Thalheim, B. (eds.) ER Workshops 2000. LNCS, vol. 1921, pp. 31–39. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  27. 27.
    Schimm, G.: Process miner - A tool for mining process schemes from event-based data. In: Flesca, S., Greco, S., Leone, N., Ianni, G. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, pp. 525–528. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  28. 28.
    Schimm, G.: Mining most specific workflow models from event-based data. In: van der Aalst, W.M.P., ter Hofstede, A.H.M., Weske, M. (eds.) BPM 2003. LNCS, vol. 2678, pp. 25–40. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  29. 29.
    Smirnov, S., Weidlich, M., Mendling, J.: Business process model abstraction based on synthesis from well-structured behavioral profiles. Int. J. Cooperative Inf. Syst. 21(1), 55–83 (2012)CrossRefGoogle Scholar
  30. 30.
    Solé, M., Carmona, J.: Process mining from a basis of state regions. In: Lilius, J., Penczek, W. (eds.) PETRI NETS 2010. LNCS, vol. 6128, pp. 226–245. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  31. 31.
    Weidlich, M., van der Werf, J.M.: On profiles and footprints – relational semantics for petri nets. In: Haddad, S., Pomello, L. (eds.) PETRI NETS 2012. LNCS, vol. 7347, pp. 148–167. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  32. 32.
    Weidlich, M., Polyvyanyy, A., Mendling, J., Weske, M.: Causal behavioural profiles - efficient computation, applications, and evaluation. Fundam. Inform. 113(3-4), 399–435 (2011)MathSciNetzbMATHGoogle Scholar
  33. 33.
    Weijters, A., van der Aalst, W., de Medeiros, A.: Process mining with the heuristics miner-algorithm. BETA Working Paper series 166, Eindhoven University of Technology (2006)Google Scholar
  34. 34.
    Weijters, A., Ribeiro, J.: Flexible Heuristics Miner. In: CIDM, pp. 310–317. IEEE (2011)Google Scholar
  35. 35.
    Wen, L., van der Aalst, W., Wang, J., Sun, J.: Mining process models with non-free-choice constructs. Data Mining and Knowledge Discovery 15(2), 145–180 (2007)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Wen, L., Wang, J., Sun, J.: Mining invisible tasks from event logs. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds.) APWeb/WAIM 2007. LNCS, vol. 4505, pp. 358–365. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  37. 37.
    van der Werf, J., van Dongen, B., Hurkens, C., Serebrenik, A.: Process discovery using integer linear programming. Fundam. Inform. 94(3-4), 387–412 (2009)MathSciNetzbMATHGoogle Scholar
  38. 38.
    Yzquierdo-Herrera, R., Silverio-Castro, R., Lazo-Cortés, M.: Sub-process discovery: Opportunities for process diagnostics. In: Poels, G. (ed.) CONFENIS 2012. LNBIP, vol. 139, pp. 48–57. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sander J. J. Leemans
    • 1
  • Dirk Fahland
    • 1
  • Wil M. P. van der Aalst
    • 1
  1. 1.Eindhoven University of TechnologyThe Netherlands

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