Incremental Process Discovery

  • Marc Solé
  • Josep Carmona
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6900)

Abstract

Process Discovery techniques provide an automatic shift between a trace or automata model into an event-based one. In particular, the problem of deriving Petri nets from transition systems or languages has many applications, ranging from CAD for VLSI to medical applications, among others. The most popular algorithms to accomplish this task are based on the theory of regions. However, one of the problems of such algorithms is the space requirements: for real-life or industrial instances, some of the region-based algorithms cannot handle in memory the internal representation of the input or the exploration lattice required. In this paper, the incremental derivation of a basis of regions and the later partitioned basis exploration are presented, which allow splitting large inputs in fragments of tractable size. The theory of the paper has been implemented as the new tool dbminer. Experimental results on medium-sized benchmarks show promising reductions in the time required for process discovery when compared to other region-based approaches.

Keywords

Transition System Region Basis Shared State Minimal Region Reachability Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    van der Aalst, W.M.P.: Process Mining. Springer, Heidelberg (2011)MATHCrossRefGoogle Scholar
  2. 2.
    Murata, T.: Petri Nets: Properties, analysis and applications. Proceedings of the IEEE, 541–580 (April 1989)Google Scholar
  3. 3.
    van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE TKDE 16(9), 1128–1142 (2004)Google Scholar
  4. 4.
    van der Aalst, W.M.P., de Medeiros, A.K.A., Weijters, A.J.M.M.T.: Genetic Process Mining. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 48–69. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Ehrenfeucht, A., Rozenberg, G.: Partial (Set) 2-Structures. Part I, II. Acta Informatica 27, 315–368 (1990)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Dongen, B.F.V., Busi, N., Pinna, G.M., van der Aalst, W.: An iterative algorithm for applying the theory of regions in process mining. In: FABPWS, pp. 36–55 (2007)Google Scholar
  7. 7.
    Carmona, J., Cortadella, J., Kishinevsky, M.: New region-based algorithms for deriving bounded Petri nets. IEEE Transactions on Computers 59(3) (2009)Google Scholar
  8. 8.
    Bergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Process Mining Based on Regions of Languages. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 375–383. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Carmona, J., Cortadella, J., Kishinevsky, M., Kondratyev, A., Lavagno, L., Yakovlev, A.: A Symbolic Algorithm for the Synthesis of Bounded Petri Nets. In: van Hee, K.M., Valk, R. (eds.) PETRI NETS 2008. LNCS, vol. 5062, pp. 92–111. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Badouel, E., Bernardinello, L., Darondeau, P.: Polynomial Algorithms for the Synthesis of Bounded Nets. In: Mosses, P.D., Nielsen, M. (eds.) CAAP 1995, FASE 1995, and TAPSOFT 1995. LNCS, vol. 915, pp. 364–383. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  11. 11.
    Hoogers, P.W., Kleijn, H.C.M., Thiagarajan, P.S.: An event structure semantics for general Petri nets. Theor. Comput. Sci. 153(1&2), 129–170 (1996)MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Desel, J., Reisig, W.: The synthesis problem of Petri nets. Acta Inf. 33(4), 297–315 (1996)MathSciNetCrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    Mukund, M.: Petri nets and step transition systems. Int. Journal of Foundations of Computer Science 3(4), 443–478 (1992)MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Darondeau, P.: Deriving Unbounded Petri Nets from Formal Languages. In: Sangiorgi, D., de Simone, R. (eds.) CONCUR 1998. LNCS, vol. 1466, pp. 533–548. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  16. 16.
    Bergenthum, R., Desel, J., Lorenz, R., Mauser, S.: Synthesis of Petri nets from finite partial languages. Fundam. Inform. 88(4), 437–468 (2008)MathSciNetMATHGoogle Scholar
  17. 17.
    Caillaud, B.: Synet : A synthesizer of distributable bounded Petri-nets from finite automata (2002), http://www.irisa.fr/s4/tools/synet/
  18. 18.
    Cortadella, J., Kishinevsky, M., Lavagno, L., Yakovlev, A.: Deriving Petri nets from finite transition systems. IEEE Trans. on Computers 47(8), 859–882 (1998)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Bryant, R.: Graph-based algorithms for Boolean function manipulation. IEEE Transactions on Computer-Aided Design 35(8), 677–691 (1986)MATHGoogle Scholar
  20. 20.
    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
  21. 21.
    van der Aalst, W., Rubin, V., Verbeek, H., van Dongen, B., Kindler, E., Günther, C.: Process mining: a two-step approach to balance between underfitting and overfitting. Software and Systems Modeling 9, 87–111 (2010)CrossRefGoogle Scholar
  22. 22.
    Bernardinello, L., Michelis, G.D., Petruni, K., Vigna, S.: On the synchronic structure of transition systems. In: Structures in Concurrency Theory, 69–84 (1995)Google Scholar
  23. 23.
    Schrijver, A.: Theory of Linear and Integer Programming. John Wiley & Sons, Chichester (1986)MATHGoogle Scholar
  24. 24.
  25. 25.
    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
  26. 26.
    Solé, M., Carmona, J.: Rbminer: A Tool for Discovering Petri Nets from Transition Systems. In: Bouajjani, A., Chin, W.-N. (eds.) ATVA 2010. LNCS, vol. 6252, pp. 396–402. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  27. 27.
    van der Aalst, W.M.P., van Dongen, B.F., Günther, C.W., Mans, R.S., de Medeiros, A.K.A., Rozinat, A., Rubin, V., Song, M., Verbeek, H.M.W(E.), Weijters, A.J.M.M.T.: 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
  28. 28.
    Eindhoven University of Technology: Process mining wiki, http://www.processmining.org
  29. 29.
    Muñoz-Gama, J., Carmona, J.: A Fresh Look at Precision in Process Conformance. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, pp. 211–226. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marc Solé
    • 1
    • 2
  • Josep Carmona
    • 2
  1. 1.Computer Architecture DepartmentUPCSpain
  2. 2.Software DepartmentUPCSpain

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