Advertisement

Big Data Meets Process Science: Distributed Mining of MP-Declare Process Models

  • Christian SturmEmail author
  • Stefan Schönig
Conference paper
  • 197 Downloads
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 363)

Abstract

Process mining techniques allow the user to build a process model representing the process behavior as recorded in the logs. Standard process discovery techniques produce as output a procedural process model. Recently, several approaches have been developed to extract declarative process models from logs and have been proven to be more suitable to analyze flexible processes, which frequently depend on human decisions and are less predictable. However, when analyzing declarative constraints from other perspective than the control flow, such as data and resources, existing process mining techniques turned out to be inefficient. Thus, computational performance remains a key challenge of declarative process discovery. In this paper, we present a high-performance approach for the discovery of multi-perspective declarative process models that is built upon the distributed big data processing method MapReduce. Compared to recent work we provide an in-depth analysis of an implementation approach based on Hadoop, a powerful BigData-Framework, and describe detailed information on the implemented prototype. We evaluated effectiveness and efficiency of the approach on real-life event logs.

Keywords

Multi-perspective process mining Declarative processes MapReduce Hadoop 

References

  1. 1.
    van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-19345-3CrossRefzbMATHGoogle Scholar
  2. 2.
    Bose, R.P.J.C., Maggi, F.M., van der Aalst, W.M.P.: Enhancing declare maps based on event correlations. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 97–112. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40176-3_9CrossRefGoogle Scholar
  3. 3.
    Burattin, A., Maggi, F.M., Sperduti, A.: Conformance checking based on multi-perspective declarative process models. Expert Syst. Appl. 65, 194–211 (2016)CrossRefGoogle Scholar
  4. 4.
    Burattin, A., Maggi, F.M., van der Aalst, W.M., Sperduti, A.: Techniques for a posteriori analysis of declarative processes. In: EDOC, pp. 41–50. IEEE, Beijing (2012)Google Scholar
  5. 5.
    Di Ciccio, C., Maggi, F.M., Montali, M., Mendling, J.: Ensuring model consistency in declarative process discovery. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 144–159. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-23063-4_9CrossRefGoogle Scholar
  6. 6.
    Di Ciccio, C., Mecella, M.: A two-step fast algorithm for the automated discovery of declarative workflows. In: CIDM, pp. 135–142. IEEE (2013)Google Scholar
  7. 7.
    Di Ciccio, C., Mecella, M.: On the discovery of declarative control flows for artful processes. ACM TMIS 5(4), 1–37 (2015)CrossRefGoogle Scholar
  8. 8.
    Di Ciccio, C., Schouten, M.H.M., de Leoni, M., Mendling, J.: Declarative process discovery with MINERful in ProM. In: BPM Demos, pp. 60–64 (2015)Google Scholar
  9. 9.
    de Leoni, M., van der Aalst, W.M.P., Dees, M.: A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf. Syst. 56, 235–257 (2016)CrossRefGoogle Scholar
  10. 10.
    Maggi, F.M.: Declarative process mining with the declare component of ProM. In: BPM Demo Sessions 2013, pp. 26–30 (2013)Google Scholar
  11. 11.
    Maggi, F.M.: Discovering metric temporal business constraints from event logs. In: Johansson, B., Andersson, B., Holmberg, N. (eds.) BIR 2014. LNBIP, vol. 194, pp. 261–275. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11370-8_19CrossRefGoogle Scholar
  12. 12.
    Maggi, F.M., Dumas, M., García-Bañuelos, L., Montali, M.: Discovering data-aware declarative process models from event logs. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 81–96. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40176-3_8CrossRefGoogle Scholar
  13. 13.
    Maggi, F.M., Mooij, A., van der Aalst, W.: User-guided discovery of declarative process models. In: CIDM, pp. 192–199 (2011)Google Scholar
  14. 14.
    Maggi, F.M., Bose, R.P.J.C., van der Aalst, W.M.P.: A knowledge-based integrated approach for discovering and repairing declare maps. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 433–448. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-38709-8_28CrossRefGoogle Scholar
  15. 15.
    Montali, M., Chesani, F., Mello, P., Maggi, F.M.: Towards data-aware constraints in declare. In: SAC, pp. 1391–1396. ACM (2013)Google Scholar
  16. 16.
    Montali, M., Pesic, M., van der Aalst, W.M.P., Chesani, F., Mello, P., Storari, S.: Declarative specification and verification of service choreographies. ACM Trans. Web 4(1), 3 (2010)CrossRefGoogle Scholar
  17. 17.
    Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: Declare: full support for loosely-structured processes. In: IEEE International EDOC Conference 2007, pp. 287–300 (2007)Google Scholar
  18. 18.
    Pichler, P., Weber, B., Zugal, S., Pinggera, J., Mendling, J., Reijers, H.A.: Imperative versus declarative process modeling languages: an empirical investigation. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 383–394. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-28108-2_37CrossRefGoogle Scholar
  19. 19.
    Räim, M., Di Ciccio, C., Maggi, F.M., Mecella, M., Mendling, J.: Log-based understanding of business processes through temporal logic query checking. In: Meersman, R., et al. (eds.) OTM 2014. LNCS, vol. 8841, pp. 75–92. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-45563-0_5 CrossRefGoogle Scholar
  20. 20.
    Schönig, S., Cabanillas, C., Jablonski, S., Mendling, J.: A framework for efficiently mining the organisational perspective of business processes. Decis. Support Syst. (2016)Google Scholar
  21. 21.
    Schönig, S., Di Ciccio, C., Maggi, F.M., Mendling, J.: Discovery of multi-perspective declarative process models. In: Service-Oriented Computing, ICSOC, Banff, pp. 87–103 (2016)CrossRefGoogle Scholar
  22. 22.
    Sturm, C., Schönig, S., Ciccio, C.D.: Distributed multi-perspective declare discovery. In: BPM Demos (2017)Google Scholar
  23. 23.
    Sturm, C., Schönig, S., Jablonski, S.: A MapReduce approach for mining multi-perspective declarative process models. In: ICEIS, no. 2, pp. 585–595 (2018)Google Scholar
  24. 24.
    Westergaard, M., Maggi, F.M.: Looking into the future. In: Meersman, R., et al. (eds.) OTM 2012. LNCS, vol. 7565, pp. 250–267. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33606-5_16CrossRefGoogle Scholar
  25. 25.
    Westergaard, M., Stahl, C., Reijers, H.: UnconstrainedMiner: efficient discovery of generalized declarative process models. In: BPM CR, No. BPM-13-28 (2013)Google Scholar
  26. 26.
    Zeising, M., Schönig, S., Jablonski, S.: Towards a common platform for the support of routine and agile business processes. In: Collaborative Computing: Networking, Applications and Worksharing (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of BayreuthBayreuthGermany

Personalised recommendations