Online Techniques for Dealing with Concept Drift in Process Mining

  • Josep Carmona
  • Ricard Gavaldà
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)

Abstract

Concept drift is an important concern for any data analysis scenario involving temporally ordered data. In the last decade Process mining arose as a discipline that uses the logs of information systems in order to mine, analyze and enhance the process dimension. There is very little work dealing with concept drift in process mining. In this paper we present the first online mechanism for detecting and managing concept drift, which is based on abstract interpretation and sequential sampling, together with recent learning techniques on data streams.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bifet, A., Gavaldà, R.: Learning from time-changing data with adaptive windowing. In: SDM. SIAM (2007)Google Scholar
  2. 2.
    Bifet, A., Gavaldà, R.: Adaptive Learning from Evolving Data Streams. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 249–260. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Bifet, A., Gavaldà, R.: Mining frequent closed trees in evolving data streams. Intell. Data Anal. 15(1), 29–48 (2011)Google Scholar
  4. 4.
    Bifet, A., Holmes, G., Pfahringer, B., Gavaldà, R.: Mining frequent closed graphs on evolving data streams. In: Apté, C., Ghosh, J., Smyth, P. (eds.) KDD, pp. 591–599. ACM (2011)Google Scholar
  5. 5.
    Jagadeesh Chandra Bose, R.P., van der Aalst, W.M.P., Žliobaitė, I.e., Pechenizkiy, M.: Handling Concept Drift in Process Mining. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 391–405. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Jagadeesh Chandra Bose, R.P.: Process Mining in the Large: Preprocessing, Discovery, and Diagnostics. PhD thesis, Eindhoven University of Technology (2012)Google Scholar
  7. 7.
    Carmona, J., Cortadella, J.: Process Mining Meets Abstract Interpretation. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010, Part I. LNCS, vol. 6321, pp. 184–199. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Carmona, J., Cortadella, J., Kishinevsky, M.: New region-based algorithms for deriving bounded Petri nets. IEEE Trans. on Computers 59(3), 371–384 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Cousot, P., Cousot, R.: Static determination of dynamic properties of programs. In: 2nd Int. Symposium on Programming, Paris, France, pp. 106–130 (1976)Google Scholar
  10. 10.
    Cousot, P., Cousot, R.: Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints. In: Proc. ACM SIGPLAN-SIGACT Symp. on Principles of Programming Languages, pp. 238–252. ACM Press (1977)Google Scholar
  11. 11.
    Cousot, P., Halbwachs, N.: Automatic discovery of linear restraints among variables of a program. In: Proc. ACM SIGPLAN-SIGACT Symp. on Principles of Programming Languages, pp. 84–97. ACM Press, New York (1978)Google Scholar
  12. 12.
    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
  13. 13.
    Jeannet, B., Miné, A.: Apron: A Library of Numerical Abstract Domains for Static Analysis. In: Bouajjani, A., Maler, O. (eds.) CAV 2009. LNCS, vol. 5643, pp. 661–667. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Miné, A.: The octagon abstract domain. In: IEEE Analysis, Slicing and Tranformation, pp. 310–319. IEEE CS Press (October 2001)Google Scholar
  15. 15.
    Murata, T.: Petri nets: Properties, analysis and applications. Proc. of the IEEE 77(4) (1989)Google Scholar
  16. 16.
    van der Aalst, W., et al.: Process Mining Manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer (2011)Google Scholar
  18. 18.
    van der Aalst, W.M.P., Günther, C.W.: Finding structure in unstructured processes: The case for process mining. In: Basten, T., Juhás, G., Shukla, S.K. (eds.) ACSD, pp. 3–12. IEEE Computer Society (2007)Google Scholar
  19. 19.
    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
  20. 20.
    Weijters, A.J.M.M., Ribeiro, J.T.S.: Flexible heuristics miner (FHM). In: CIDM, pp. 310–317 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Josep Carmona
    • 1
  • Ricard Gavaldà
    • 1
  1. 1.Universitat Politècnica de CatalunyaBarcelonaSpain

Personalised recommendations