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The Effect of Noise on Mined Declarative Constraints

  • Claudio Di Ciccio
  • Massimo Mecella
  • Jan Mendling
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 203)

Abstract

Declarative models are increasingly utilized as representational format in process mining. Models created from automatic process discovery are meant to summarize complex behaviors in a compact way. Therefore, declarative models do not define all permissible behavior directly, but instead define constraints that must be met by each trace of the business process. While declarative models provide compactness, it is up until now not clear how robust or sensitive different constraints are with respect to noise. In this paper, we investigate this question from two angles. First, we establish a constraint hierarchy based on formal relationships between the different types of Declare constraints. Second, we conduct a sensitivity analysis to investigate the effect of noise on different types of declarative rules. Our analysis reveals that an increasing degree of noise reduces support of many constraints. However, this effect is moderate on most of the constraint types, which supports the suitability of Declare for mining event logs with noise.

Keywords

Process mining Declarative workflows Noisy event logs 

References

  1. 1.
    van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Fahland, D., Mendling, J., Reijers, H.A., Weber, B., Weidlich, M., Zugal, S.: Declarative versus imperative process modeling languages: the issue of maintainability. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 477–488. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  3. 3.
    Fahland, D., Lübke, D., Mendling, J., Reijers, H., Weber, B., Weidlich, M., Zugal, S.: Declarative versus imperative process modeling languages: the issue of understandability. In: Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Ukor, R. (eds.) Enterprise, Business-Process and Information Systems Modeling. LNBIP, vol. 29, pp. 353–366. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Weijters, A.J.M.M., van der Aalst, W.M.P.: Rediscovering workflow models from event-based data using little thumb. Integr. Comput. Aided Eng. 10(2), 151–162 (2003)Google Scholar
  6. 6.
    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
  7. 7.
    de Medeiros, A.K.A., Weijters, A.J.M.M., van der Aalst, W.M.P.: Genetic process mining: an experimental evaluation. Data Min. Knowl. Discov. 14(2), 245–304 (2007)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Di Ciccio, C., Mecella, M., Scannapieco, M., Zardetto, D., Catarci, T.: MailOfMine – analyzing mail messages for mining artful collaborative processes. In: Aberer, K., Damiani, E., Dillon, T. (eds.) SIMPDA 2011. LNBIP, vol. 116, pp. 55–81. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  9. 9.
    Pesic, M., van der Aalst, W.M.P.: A declarative approach for flexible business processes management. In: Eder, J., Dustdar, S. (eds.) BPM Workshops 2006. LNCS, vol. 4103, pp. 169–180. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  10. 10.
    Pesic, M.: Constraint-based workflow management systems: shifting control to users. Ph.D. thesis, Technische Universiteit Eindhoven (2008)Google Scholar
  11. 11.
    van der Aalst, W.M.P., Pesic, M., Schonenberg, H.: Declarative workflows: balancing between flexibility and support. Comput. Sci. - R&D 23(2), 99–113 (2009)Google Scholar
  12. 12.
    van der Aalst, W.M.P., Pesic, M.: DecSerFlow: towards a truly declarative service flow language. In: Bravetti, M., Núñez, M., Zavattaro, G. (eds.) WS-FM 2006. LNCS, vol. 4184, pp. 1–23. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  13. 13.
    Maggi, F.M., Mooij, A.J., van der Aalst, W.M.P.: User-guided discovery of declarative process models. In: CIDM, pp. 192–199. IEEE (2011)Google Scholar
  14. 14.
    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
  15. 15.
    Di Ciccio, C., Mecella, M.: On the discovery of declarative control flows for artful processes. ACM Trans. Manage. Inf. Syst. 5(4), 24:1–24:37 (2015)CrossRefGoogle Scholar
  16. 16.
    De Giacomo, G., Vardi, M.Y.: Linear temporal logic and linear dynamic logic on finite traces. In: IJCAI, pp. 854–860 (2013)Google Scholar
  17. 17.
    Prescher, J., Di Ciccio, C., Mendling, J.: From declarative processes to imperative models. In: SIMPDA, vol. 1293, pp. 162–173 (2014). CEUR-WS.org
  18. 18.
    van der Aalst, W.M.P., van Dongen, B.F., Günther, C.W., Rozinat, A., Verbeek, E., Weijters, T.: ProM: the process mining toolkit. In: BPM (Demos) (2009)Google Scholar
  19. 19.
    Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 467–483. Springer, Heidelberg (1998) CrossRefGoogle Scholar
  20. 20.
    van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  21. 21.
    Wen, L., van der Aalst, W.M.P., Wang, J., Sun, J.: Mining process models with non-free-choice constructs. Data Min. Knowl. Discov. 15(2), 145–180 (2007)CrossRefMathSciNetGoogle Scholar
  22. 22.
    van der Aalst, W.M.P., Rubin, V., Verbeek, E., van Dongen, B.F., Kindler, E., Günther, C.W.: Process mining: a two-step approach to balance between underfitting and overfitting. Softw. Syst. Model. 9, 87–111 (2010)CrossRefGoogle Scholar
  23. 23.
    Cortadella, J., Kishinevsky, M., Lavagno, L., Yakovlev, A.: Deriving petri nets from finite transition systems. IEEE Trans. Comput. 47(8), 859–882 (1998)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Desel, J., Reisig, W.: The synthesis problem of petri nets. Acta Informatica 33, 297–315 (1996)CrossRefzbMATHMathSciNetGoogle Scholar
  25. 25.
    Fahland, D., van der Aalst, W.M.P.: Repairing process models to reflect reality. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 229–245. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  26. 26.
    Fahland, D., van der Aalst, W.M.P.: Model repair - aligning process models to reality. Inf. Syst. 47, 220–243 (2015)CrossRefGoogle Scholar
  27. 27.
    Di Ciccio, C., Marrella, A., Russo, A.: Knowledge-intensive processes: characteristics, requirements and analysis of contemporary approaches. J. Data Semant. 1–29 (2014). doi: 10.1007/s13740-014-0038-4
  28. 28.
    Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: Declare: Full support for loosely-structured processes. In: EDOC, pp. 287–300 (2007)Google Scholar
  29. 29.
    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
  30. 30.
    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
  31. 31.
    Chesani, F., Lamma, E., Mello, P., Montali, M., Riguzzi, F., Storari, S.: Exploiting inductive logic programming techniques for declarative process mining. In: Jensen, K., van der 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
  32. 32.
    Bellodi, E., Riguzzi, F., Lamma, E.: Probabilistic logic-based process mining. In: CILC (2010)Google Scholar
  33. 33.
    Bellodi, E., Riguzzi, F., Lamma, E.: Probabilistic declarative process mining. In: Bi, Y., Williams, M.-A. (eds.) KSEM 2010. LNCS, vol. 6291, pp. 292–303. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  34. 34.
    Montali, M.: Declarative open interaction models. In: Montali, M. (ed.) Specification and Verification of Declarative Open Interaction Models. LNBIP, vol. 56, pp. 11–45. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  35. 35.
    Di Ciccio, C., Mecella, M.: Mining constraints for artful processes. In: Abramowicz, W., Kriksciuniene, D., Sakalauskas, V. (eds.) BIS 2012. LNBIP, vol. 117, pp. 11–23. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  36. 36.
    de Leoni, M., Maggi, F.M., van der Aalst, W.M.P.: An alignment-based framework to check the conformance of declarative process models and to preprocess event-log data. Inf. Syst. 47, 258–277 (2015)CrossRefGoogle Scholar
  37. 37.
    Rogge-Solti, A., Mans, R.S., van der Aalst, W.M.P., Weske, M.: Repairing event logs using timed process models. In: Demey, Y.T., Panetto, H. (eds.) OTM 2013 Workshops 2013. LNCS, vol. 8186, pp. 705–708. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  38. 38.
    Rogge-Solti, A., Mans, R.S., van der Aalst, W.M.P., Weske, M.: Improving documentation by repairing event logs. In: Grabis, J., Kirikova, M., Zdravkovic, J., Stirna, J. (eds.) PoEM 2013. LNBIP, vol. 165, pp. 129–144. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  39. 39.
    Rogge-Solti, A.: Probabilistic Estimation of Unobserved Process Events. Ph.D. thesis, Hasso Plattner Institute at the University of Potsdam, Germany (2014)Google Scholar
  40. 40.
    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
  41. 41.
    Schunselaar, D.M.M., Maggi, F.M., Sidorova, N.: Patterns for a log-based strengthening of declarative compliance models. In: Derrick, J., Gnesi, S., Latella, D., Treharne, H. (eds.) IFM 2012. LNCS, vol. 7321, pp. 327–342. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  42. 42.
    van der Spoel, S., van Keulen, M., Amrit, C.: Process prediction in noisy data sets: a case study in a dutch hospital. In: Cudre-Mauroux, P., Ceravolo, P., Gašević, D. (eds.) SIMPDA 2012. LNBIP, vol. 162, pp. 60–83. Springer, Heidelberg (2013) CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Claudio Di Ciccio
    • 1
  • Massimo Mecella
    • 2
  • Jan Mendling
    • 1
  1. 1.Wirtschaftsuniversität WienViennaAustria
  2. 2.Sapienza – Università di RomaRomeItaly

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