Efficient Construction of Behavior Graphs for Uncertain Event Data

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 389)


The discipline of process mining deals with analyzing execution data of operational processes, extracting models from event data, checking the conformance between event data and normative models, and enhancing all aspects of processes. Recently, new techniques have been developed to analyze event data containing uncertainty; these techniques strongly rely on representing uncertain event data through graph-based models capturing uncertainty. In this paper we present a novel approach to efficiently compute a graph representation of the behavior contained in an uncertain process trace. We present our new algorithm, analyze its time complexity, and report experimental results showing order-of-magnitude performance improvements for behavior graph construction.


Process mining Uncertain data Event data representation 


  1. 1.
    Van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer, Cham (2016)CrossRefGoogle Scholar
  2. 2.
    Aho, A., et al.: Compilers: Principles, Techniques and Tools (2007)Google Scholar
  3. 3.
    Aho, A.V., Garey, M.R., Ullman, J.D.: The transitive reduction of a directed graph. SIAM J. Comput. 1(2), 131–137 (1972)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Al-Mutawa, H.A., Dietrich, J., Marsland, S., McCartin, C.: On the shape of circular dependencies in Java programs. In: 2014 23rd Australian Software Engineering Conference, pp. 48–57. IEEE (2014)Google Scholar
  5. 5.
    Bayes, T.: LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, FRS communicated by Mr. Price, in a letter to John Canton, AMFR S. Philos. Trans. R. Soc. Lond. (53), 370–418 (1763)Google Scholar
  6. 6.
    Berti, A., van Zelst, S.J., van der Aalst, W.M.P.: Process Mining for Python (PM4Py): bridging the gap between process- and data science. In: ICPM Demo Track (CEUR 2374), pp. 13–16 (2019)Google Scholar
  7. 7.
    D’Alberto, P., Nicolau, A.: Using recursion to boost ATLAS’s performance. In: Labarta, J., Joe, K., Sato, T. (eds.) ALPS/ISHPC 2005-2006. LNCS, vol. 4759, pp. 142–151. Springer, Heidelberg (2008). Scholar
  8. 8.
    Dutta, S.: An event based fuzzy temporal logic. In: 1988 Proceedings of the Eighteenth International Symposium on Multiple-Valued Logic, pp. 64–71. IEEE (1988)Google Scholar
  9. 9.
    Le Gall, F.: Faster algorithms for rectangular matrix multiplication. In: 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science, pp. 514–523. IEEE (2012)Google Scholar
  10. 10.
    Le Gall, F.: Powers of tensors and fast matrix multiplication. In: Proceedings of the 39th International Symposium on Symbolic and Algebraic Computation, pp. 296–303. ACM (2014)Google Scholar
  11. 11.
    Mariappan, M., Vora, K.: GraphBolt: dependency-driven synchronous processing of streaming graphs. In: Proceedings of the Fourteenth EuroSys Conference 2019, p. 25. ACM (2019)Google Scholar
  12. 12.
    Pegoraro, M., van der Aalst, W.M.P.: Mining uncertain event data in process mining. In: 2019 International Conference on Process Mining (ICPM), pp. 89–96. IEEE (2019)Google Scholar
  13. 13.
    Pegoraro, M., Uysal, M.S., van der Aalst, W.M.P.: Discovering process models from uncertain event data. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds.) BPM 2019. LNBIP, vol. 362, pp. 238–249. Springer, Cham (2019). Scholar
  14. 14.
    Strassen, V.: Gaussian elimination is not optimal. Numer. Math. 13(4), 354–356 (1969)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Process and Data Science Group (PADS) Department of Computer ScienceRWTH Aachen UniversityAachenGermany

Personalised recommendations