Mining Constraints for Artful Processes

  • Claudio Di Ciccio
  • Massimo Mecella
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 117)


Artful processes are informal processes typically carried out by those people whose work is mental rather than physical (managers, professors, researchers, engineers, etc.), the so called “knowledge workers”. MailOfMine is a tool, the aim of which is to automatically build, on top of a collection of email messages, a set of workflow models that represent the artful processes laying behind the knowledge workers activities. After an outline of the approach and the tool, this paper focuses on the mining algorithm, able to efficiently compute the set of constraints describing the artful process. Finally, an experimental evaluation of it is reported.


process mining artful process declarative workflow email 


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  1. 1.
    van der Aalst, W., Rubin, V., Verbeek, H., van Dongen, B., Kindler, E., Günther, C.: Process mining: a two-step approach to balance between underfitting and overfitting. Software and Systems Modeling 9, 87–111 (2010)CrossRefGoogle Scholar
  2. 2.
    van der Aalst, W.M.P.: Verification of Workflow Nets. In: Azéma, P., Balbo, G. (eds.) ICATPN 1997. LNCS, vol. 1248, pp. 407–426. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  3. 3.
    van der Aalst, W.M.P.: Process mining: Discovery, conformance and enhancement of business processes. Springer (2011)Google Scholar
  4. 4.
    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 2009 Demos. CEUR Workshop Proceedings, vol. 489 (2009)Google Scholar
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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. 469–483. Springer, Heidelberg (1998)Google Scholar
  8. 8.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: ICDE 1995, pp. 3–14 (1995)Google Scholar
  9. 9.
    Alberti, M., Chesani, F., Gavanelli, M., Lamma, E., Mello, P., Torroni, P.: Verifiable agent interaction in abductive logic programming: The SCIFF framework. ACM Trans. Comput. Log. 9(4) (2008)Google Scholar
  10. 10.
    de Carvalho, V.R., Cohen, W.W.: Learning to extract signature and reply lines from email. In: CEAS 2004 (2004)Google Scholar
  11. 11.
    Catarci, T., Dix, A., Katifori, A., Lepouras, G., Poggi, A.: Task-Centred Information Management. In: Thanos, C., Borri, F., Candela, L. (eds.) Digital Libraries: Research and Development. LNCS, vol. 4877, pp. 197–206. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Chesani, F., Lamma, E., Mello, P., Montali, M., Riguzzi, F., Storari, S.: Exploiting inductive logic programming techniques for declarative process mining. T. Petri Nets and Other Models of Concurrency 2, 278–295 (2009)CrossRefGoogle Scholar
  13. 13.
    Chomsky, N., Miller, G.A.: Finite state languages. Information and Control 1(2), 91–112 (1958)CrossRefGoogle Scholar
  14. 14.
    Di Ciccio, C., Mecella, M., Catarci, T.: Representing and Visualizing Mined Artful Processes in MailOfMine. In: Holzinger, A., Simonic, K.-M. (eds.) USAB 2011. LNCS, vol. 7058, pp. 83–94. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Di Ciccio, C., Mecella, M.: MINERful, a mining algorithm for declarative process constraints in MailOfMine. Tech. rep. SAPIENZA Università di Roma (2012),
  16. 16.
    Di Ciccio, C., Mecella, M., Scannapieco, M., Zardetto, D., Catarci, T.: MailOfMine – Analyzing mail messages for mining artful collaborative processes. In: SIMPDA 2011, pp. 45–59 (2011)Google Scholar
  17. 17.
    Garofalakis, M.N., Rastogi, R., Shim, K.: SPIRIT: Sequential pattern mining with regular expression constraints. In: VLDB 1999, pp. 223–234 (1999)Google Scholar
  18. 18.
    Gerth, R., Peled, D., Vardi, M.Y., Wolper, P.: Simple on-the-fly automatic verification of linear temporal logic. In: PSTV 1995, pp. 3–18 (1995)Google Scholar
  19. 19.
    Giannakopoulou, D., Havelund, K.: Automata-based verification of temporal properties on running programs. In: ASE 2001, pp. 412–416 (2001)Google Scholar
  20. 20.
    Heutelbeck, D.: Preservation of Enterprise Engineering Processes by Social Collaboration Software. In: Altmann, J., Baumöl, U., Krämer, B.J. (eds.) Advances in Collective Intelligence 2011. AISC, vol. 113, pp. 115–132. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    Maggi, F.M., Mooij, A.J., van der Aalst, W.M.P.: User-guided discovery of declarative process models. In: CIDM 2011, pp. 192–199 (2011)Google Scholar
  22. 22.
    Medeiros, A.K., Weijters, A.J., Aalst, W.M.: Genetic process mining: an experimental evaluation. Data Min. Knowl. Discov. 14(2), 245–304 (2007)CrossRefGoogle Scholar
  23. 23.
    Myers, E.W.: An O(ND) difference algorithm and its variations. Algorithmica 1(2), 251–266 (1986)CrossRefGoogle Scholar
  24. 24.
    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
  25. 25.
    Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: Declare: Full support for loosely-structured processes. In: EDOC 2007, pp. 287–300 (2007)Google Scholar
  26. 26.
    Pesic, M., Schonenberg, M.H., Sidorova, N., van der Aalst, W.M.P.: Constraint-Based Workflow Models: Change Made Easy. In: Meersman, R., Tari, Z. (eds.) OTM 2007, Part I. LNCS, vol. 4803, pp. 77–94. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  27. 27.
    Rabin, M.O., Scott, D.: Finite automata and their decision problems. IBM J. Res. Dev. 3, 114–125 (1959)CrossRefGoogle Scholar
  28. 28.
    Smart Vortex Consortium: Smart Vortex – Management and analysis of massive data streams to support large-scale collaborative engineering projects. FP7 IP Project,
  29. 29.
    Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)Google Scholar
  30. 30.
    Warren, P., Kings, N., Thurlow, I., Davies, J., Buerger, T., Simperl, E., Ruiz, C., Gomez-Perez, J.M., Ermolayev, V., Ghani, R., Tilly, M., Bösser, T., Imtiaz, A.: Improving knowledge worker productivity - the Active integrated approach. BT Technology Journal 26(2), 165–176 (2009)Google Scholar
  31. 31.
    Weijters, A., van der Aalst, W.: Rediscovering workflow models from event-based data using little thumb. Integrated Computer-Aided Engineering 10 (2001, 2003)Google Scholar
  32. 32.
    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)CrossRefGoogle Scholar
  33. 33.
    Westergaard, M.: Better Algorithms for Analyzing and Enacting Declarative Workflow Languages Using LTL. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 83–98. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Claudio Di Ciccio
    • 1
  • Massimo Mecella
    • 1
  1. 1.Dipartimento di Ingegneria Informatica, Automatica e Gestionale ANTONIO RUBERTISAPIENZA – Università di RomaItaly

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