Process discovery: Capturing the invisible

IEEE Computational Intelligence Magazine

Volumn 5, Issue 1, 2010, Pages 28-41

  Van De Aalst, Will ^a  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BUSINESS PROCESS; COMPLEX PROCESSES; COMPLEX REALITY; COMPUTATIONAL INTELLIGENCE; DAILY LIVES; DYNAMIC NATURE; EBUSINESS; ENTERPRISE INFORMA-TION SYSTEMS; EVENT LOGS; HIGH-TECH SYSTEMS; MEDICAL DEVICES; OPERATIONAL PROCESS; PROCESS DISCOVERY; PROCESS MINING; REAL PROCESS;

ARTIFICIAL INTELLIGENCE; ELECTRONIC COMMERCE;

KNOWLEDGE BASED SYSTEMS;

EID: 76349087168     PISSN: 1556603X     EISSN: None     Source Type: Journal    
DOI: 10.1109/MCI.2009.935307     Document Type: Article

References (32)

1. W. Van Der Aalst, H. Reijers, A. Weijters, B. Van Dongen, A. Medeiros, M. Song, and H. Verbeek, "Business process mining: An industrial application," Inform. Syst., vol.32, no.5, pp. 713-732, 2007.
2. R. Agrawal and R. Srikant, "Mining sequential patterns," in Proc. 11th Int. Conf. Data Engineering (ICDE'95), IEEE Computer Society, 1995, pp. 3-14.
3. W. Van Der Aalst, A. Weijters, and L. Maruster, "Workf low mining: Discovering process models from event logs," IEEE Trans. Knowledge and Data Engineering, vol.16, no.9, pp. 1128-1142, 2004.
4. A. Rozinat and W. Van Der Aalst, "Conformance checking of processes based on monitoring real behavior," Inform. Syst., vol.33, no.1, pp. 64-95, 2008.
5. A. Rozinat and W. Van Der Aalst, "Decision mining in ProM," in Proc. Int. Conf. Business Process Management (BPM'06) (Lecture Notes in Computer Science), vol.4102, S. Dustdar, J. Fiadeiro, and A. Sheth, Eds. Berlin: Springer-Verlag, 2006, pp. 420-425.
6. W. Van Der Aalst, B. Van Dongen, C. Günther, R. Mans, A. Medeiros, A. Rozinat, V. Rubin, M. Song, H. Verbeek, and A. Weijters, "ProM 4.0: Comprehensive support for real process analysis," in Proc. Application and Theory of Petri Nets and Other Models of Concurrency (ICATPN 2007) (Lecture Notes in Computer Science), vol.4546, J. Kleijn and A. Yakovlev, Eds. Berlin: Springer-Verlag, 2007, pp. 484-494.
7. W. Reisig and G. Rozenberg, Eds., Lectures on Petri Nets I: Basic Models (Lecture Notes in Computer Science), vol.1491. Berlin: Springer-Verlag, 1998.
8. W. Van Der Aalst, "The application of Petri nets to workf low management," J. Circuits, Syst. Comput., vol.8, no.1, pp. 21-66, 1998.
9. H. Verbeek, T. Basten, and W. Van Der Aalst, "Diagnosing workf low processes using Wof lan," Comput. J., vol.44, no.4, pp. 246-279, 2001.
10. R. Agrawal, D. Gunopulos, and F. Leymann, "Mining process models from workf low logs," in Proc. 6th Int. Conf. Extending Database Technology, 1998, pp. 469-483.
11. J. Cook and A. Wolf, "Discovering models of software processes from event-based data," ACM Trans. Softw. Eng. Methodol., vol.7, no.3, pp. 215-249, 1998.
12. A. Datta, "Automating the discovery of As-Is business process models: Probabilistic and algorithmic approaches," Inform. Syst. Res., vol.9, no.3, pp. 275-301, 1998.
13. B. Van Dongen and W. Van Der Aalst, "Multi-phase process mining: Building instance graphs," in Proc. Int. Conf. Conceptual Modeling (ER'04) (Lecture Notes in Computer Science), vol.3288, P. Atzeni, W. Chu, H. Lu, S. Zhou, and T. Ling, Eds. Berlin: Springer- Verlag, 2004, pp. 362-376.
14. B. Van Dongen and W. Van Der Aalst, "Multi-phase mining: Aggregating instances graphs into EPCs and Petri nets," in Proc. 2nd Int. Workshop on Applications of Petri Nets to Coordination, Workflow and Business Process Management, D. Marinescu, Ed. Miami, Florida, USA: Florida Int. Univ., 2005, pp. 35-58.
15. A. Weijters and W. Van Der Aalst, "Rediscovering workf low models from eventbased data using Little Thumb," Integr. Comput. Aided Eng., vol.10, no.2, pp. 151-162, 2003.
16. W. Van Der Aalst, B. Van Dongen, J. Herbst, L. Maruster, G. Schimm, and A. Weijters, "Workf low mining: A survey of issues and approaches," Data Knowl. Eng., vol.47, no.2, pp. 237-267, 2003.
17. J. Herbst, "A machine learning approach to workf low management," in Proc. 11th European Conf. Machine Learning (Lecture Notes in Computer Science), vol.1810. Berlin: Springer-Verlag, 2000, pp. 183-194.
18. W. Van Der Aalst, V. Rubin, B. Van Dongen, E. Kindler, and C. Günther, "Process mining: A two-step approach to balance between underfitting and overfitting," Softw. Syst. Model., 2009.
19. J. Cortadella, M. Kishinevsky, L. Lavagno, and A. Yakovlev, "Deriving Petri nets from finite transition systems," IEEE Trans. Comput., vol.47, no.8, pp. 859-882, Aug. 1998.
20. R. Bergenthum, J. Desel, R. Lorenz, and S. Mauser, "Process mining based on regions of languages," in Proc. Int. Conf. Business Process Management (BPM'07) (Lecture Notes in Computer Science), vol.4714, G. Alonso, P. Dadam, and M. Rosemann, Eds. Berlin: Springer-Verlag, 2007, pp. 375-383.
21. J. Werf, B. Van Dongen, C. Hurkens, and A. Serebrenik, "Process discovery using integer linear programming," in Proc. 29th Int. Conf. Applications and Theory of Petri Nets (Petri Nets 2008) (Lecture Notes in Computer Science), vol.5062, K. Hee and R. Valk, Eds. Berlin: Springer-Verlag, 2008, pp. 368-387.
22. D. Angluin and C. Smith, "Inductive inference: Theory and methods," Computing Surv., vol.15, no.3, pp. 237-269, 1983.
23. E. Gold, "Language identfication in the limit," Inform. Control, vol.10, no.5, pp. 447-474, 1967.
24. E. Gold, "Complexity of automaton identification from given data," Inform. Control, vol.37, no.3, pp. 302-320, 1978.
25. L. Pitt, "Inductive inference, DFAs, and computational complexity," in Proc. Int. Workshop on Analogical and Inductive Inference (AII) (Lecture Notes in Computer Science), vol.397, K. Jantke, Ed. Berlin: Springer-Verlag, 1989, pp. 18-44.
26. C. Günther and W. Van Der Aalst, "Fuzzy mining: Adaptive process simplification based on multi-perspective metrics," in Proc. Int. Conf. Business Process Management (BPM'07) (Lecture Notes in Computer Science), vol.4714, G. Alonso, P. Dadam, and M. Rosemann, Eds. Berlin: Springer-Verlag, 2007, pp. 328-343.
27. A. Medeiros, A. Weijters, and W. Van Der Aalst, "Genetic process mining: An experimental evaluation," Data Mining Knowl. Discov., vol.14, no.2, pp. 245-304, 2007.
28. A. Medeiros, W. Van Der Aalst, and A. Weijters, "Workf low mining: Current status and future directions," in Proc. On The Move to Meaning ful Internet Systems 2003: CoopIS, DOA, and ODBASE (Lecture Notes in Computer Science), vol.2888, R. Meersman, Z. Tari, and D. Schmidt, Eds. Berlin: Springer-Verlag, 2003, pp. 389-406.
29. L. Wen, W. Van Der Aalst, J. Wang, and J. Sun, "Mining process models with non-free-choice constructs," Data Mining Knowl. Discov., vol.15, no.2, pp. 145-180, 2007.
30. J. Desel and J. Esparza, Free Choice Petri Nets (Cambridge Tracts in Theoretical Computer Science), vol.40. Cambridge, U.K.: Cambridge Univ. Press, 1995.
31. W. Duch, "What is computational intelligence and where is it going?" in Challenges for Computational Intelligence (Studies in Computational Intelligence), vol.1420, W. Duch and J. Mandziuk, Eds. Berlin: Springer-Verlag, 2007, pp. 1-13.
32. A. Engelbrecht, Computational Intelligence: An Introduction. Hoboken, NJ: Wiley, 2007.