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Discovery of Multi-perspective Declarative Process Models

  • Stefan Schönig
  • Claudio Di Ciccio
  • Fabrizio M. Maggi
  • Jan Mendling
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9936)

Abstract

Process discovery is one of the main branches of process mining that allows 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 (e.g., a Petri net). Recently, several approaches have been developed to derive declarative process models from logs and have been proven to be more suitable to analyze processes working in environments that are less stable and predictable. However, a large part of these techniques are focused on the analysis of the control flow perspective of a business process. Therefore, one of the challenges still open in this field is the development of techniques for the analysis of business processes also from other perspectives, like data, time, and resources. In this paper, we present a full-fledged approach for the discovery of multi-perspective declarative process models from event logs that allows the user to discover declarative models taking into consideration all the information an event log can provide. The approach has been implemented and experimented in real-life case studies.

Keywords

Process mining Process discovery Multi-perspective process model Declarative process model Declare 

References

  1. 1.
    van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  2. 2.
    van der Aalst, W., Pesic, M., Schonenberg, H.: Declarative workflows: balancing between flexibility and support. Comput. Sci. - R&D 23, 99–113 (2009)Google Scholar
  3. 3.
    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)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, September 2012Google Scholar
  5. 5.
    Burattin, A., Maggi, F.M., Sperduti, A.: Conformance checking based on multi-perspective declarative process models (2015). CoRR arXiv:1503.04957
  6. 6.
    Chesani, F., Lamma, E., Mello, P., Montali, M., Riguzzi, F., Storari, S.: Exploiting inductive logic programming techniques for declarative process mining. In: Jensen, K., Aalst, W.M.P. (eds.) Transactions on Petri Nets and Other Models of Concurrency II. LNCS, vol. 5460, pp. 278–295. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    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, Berlin (2015)CrossRefGoogle Scholar
  8. 8.
    Di Ciccio, C., Mecella, M.: A two-step fast algorithm for the automated discovery of declarative workflows. In: CIDM, pp. 135–142. IEEE, April 2013Google Scholar
  9. 9.
    Di Ciccio, C., Mecella, M.: On the discovery of declarative control flows for artful processes. ACM TMIS 5(4), 24:1–24:37 (2015)Google Scholar
  10. 10.
    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
  11. 11.
    van Dongen, B.F., Shabani, S.: Relational XES: data management for process mining. In: CAiSE Forum 2015, pp. 169–176 (2015)Google Scholar
  12. 12.
    Hildebrandt, T.T., Mukkamala, R.R., Slaats, T., Zanitti, F.: Contracts for cross-organizational workflows as timed dynamic condition response graphs. J. Log. Algebr. Program. 82(5–7), 164–185 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Kupferman, O., Vardi, M.Y.: Vacuity detection in temporal model checking. Int. J. Softw. Tools Technol. Transf. 4, 224–233 (2003)CrossRefzbMATHGoogle Scholar
  14. 14.
    Lamma, E., Mello, P., Riguzzi, F., Storari, S.: Applying inductive logic programming to process mining. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 132–146. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Maggi, F.M.: Declarative process mining with the declare component of ProM. In: BPM Demo Sessions 2013, pp. 26–30 (2013)Google Scholar
  17. 17.
    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, Heidelberg (2014)Google Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    Maggi, F.M., Mooij, A., van der Aalst, W.: User-guided discovery of declarative process models. In: CIDM, pp. 192–199 (2011)Google Scholar
  20. 20.
    Maggi, F.M., Bose, R.P.J.C., van der Aalst, W.M.P.: Efficient discovery of understandable declarative process models from event logs. In: Ralyté, J., Franch, X., Brinkkemper, S., Wrycza, S. (eds.) CAiSE 2012. LNCS, vol. 7328, pp. 270–285. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    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)CrossRefGoogle Scholar
  22. 22.
    Montali, M., Chesani, F., Mello, P., Maggi, F.M.: Towards data-aware constraints in declare. In: SAC, pp. 1391–1396. ACM (2013)Google Scholar
  23. 23.
    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
  24. 24.
    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
  25. 25.
    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 Workshops 2011, Part I. LNBIP, vol. 99, pp. 383–394. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  26. 26.
    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., Panetto, H., Dillon, T., Missikoff, M., Liu, L., Pastor, O., Cuzzocrea, A., Sellis, T. (eds.) OTM 2014. LNCS, vol. 8841, pp. 75–92. Springer, Heidelberg (2014)Google Scholar
  27. 27.
    Rozinat, A., Mans, R.S., Song, M., van der Aalst, W.M.P.: Discovering simulation models. Inf. Syst. 34(3), 305–327 (2009)CrossRefGoogle Scholar
  28. 28.
    Schönig, S., Cabanillas, C., Jablonski, S., Mendling, J.: A framework for efficiently mining the organisational perspective of business processes. Decis. Support Syst. 89, 87–97 (2016)CrossRefGoogle Scholar
  29. 29.
    Schönig, S., Rogge-Solti, A., Cabanillas, C., Jablonski, S., Mendling, J.: Efficient and customisable declarative process mining with SQL. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 290–305. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-39696-5_18 CrossRefGoogle Scholar
  30. 30.
    Westergaard, M., Maggi, F.M.: Looking into the future. In: Meersman, R., et al. (eds.) OTM 2012, Part I. LNCS, vol. 7565, pp. 250–267. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  31. 31.
    Westergaard, M., Stahl, C., Reijers, H.: UnconstrainedMiner: efficient discovery of generalized declarative process models. In: BPM CR, No. BPM-13-28 (2013)Google Scholar
  32. 32.
    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 International Publishing Switzerland 2016

Authors and Affiliations

  • Stefan Schönig
    • 1
  • Claudio Di Ciccio
    • 1
  • Fabrizio M. Maggi
    • 2
  • Jan Mendling
    • 1
  1. 1.Vienna University of Economics and BusinessViennaAustria
  2. 2.University of TartuTartuEstonia

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