A novel approach for process mining based on event types

Journal of Intelligent Information Systems

Volumn 32, Issue 2, 2009, Pages 163-190

  Wen, Lijie ^a,b   Wang, Jianmin ^a,b   Van Der Aalst, Wil M P ^c   Huang, Biqing ^a   Sun, Jiaguang ^a,b  

^a TSINGHUA UNIVERSITY   (China)

^b TSINGHUA NATIONAL LABORATORY FOR INFORMATION SCIENCE AND TECHNOLOGY   (China)

^c EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Data mining; DWF nets; Event types; Petri nets; Process mining; WF nets; Workflow mining

Indexed keywords

GRAPH THEORY; INFORMATION MANAGEMENT; MARINE BIOLOGY; PETRI NETS;

DWF-NETS; EVENT TYPES; PROCESS MINING; WF-NETS; WORKFLOW MINING;

MINING;

EID: 62249195621     PISSN: 09259902     EISSN: 15737675     Source Type: Journal    
DOI: 10.1007/s10844-007-0052-1     Document Type: Article

References (51)

1. Agrawal, R., Gunopulos, D., & Leymann, F. (1998). Mining process models from workflow logs. In Sixth International Conference on Extending Database Technology (pp. 469-483).
2. D. Angluin C. H. Smith 1983 Inductive inference: Theory and methods Computing Surveys 15 3 37 269
3. J. E. Cook A. L. Wolf 1998 Discovering models of software processes from event-based data ACM Transactions on Software Engineering and Methodology 7 3 215 249
4. Cook, J. E., & Wolf, A. L. (1998b). Event-based detection of concurrency. In Proceedings of the Sixth International Symposium on the Foundations of Software Engineering (FSE-6) (pp. 35-45).
5. J. E. Cook A. L. Wolf 1999 Software process validation: Quantitatively measuring the correspondence of a process to a model ACM Transactions on Software Engineering and Methodology 8 2 147 176
6. Desel, J., & Esparza, J. (1995). Free choice petri nets, volume 40 of cambridge tracts in theoretical computer science. Cambridge, UK: Cambridge University Press.
7. Eder, J., Olivotto, G. E., & Gruber, W. (2002). A data warehouse for workflow logs. In Y. Han, S. Tai, & D. Wikarski (Eds.), International Conference on Engineering and Deployment of Cooperative Information Systems (EDCIS 2002), Volume 2480 of Lecture Notes in Computer Science (pp. 1-15). Berlin: Springer.
8. A. Ehrenfeucht G. Rozenberg 1989 Partial (set) 2-structures-Part 1 and part 2 Acta Informatica 27 4 315 368
9. E. M. Gold 1967 Language identfication in the limit Information and Control 10 5 447 474
10. E. M. Gold 1978 Complexity of automaton identification from given data Information and Control 37 3 302 320
11. Grigori, D., Casati, F., Dayal, U., & Shan, M. C. (2001). Improving business process quality through exception understanding, prediction, and prevention. In P. Apers, P. Atzeni, S. Ceri, S. Paraboschi, K. Ramamohanarao, & R. Snodgrass (Eds.), Proceedings of 27th International Conference on Very Large Data Bases (VLDB'01) (pp. 159-168). Morgan Kaufmann.
12. Herbst, J. (2000a). A machine learning approach to workflow management. In Proceedings 11th European Conference on Machine Learning, Volume 1810 of Lecture Notes in Computer Science (pp. 183-194). Berlin: Springer-Verlag.
13. Herbst, J. (2000b). Dealing with concurrency in workflow induction. In U. Baake, R. Zobel, & M. Al-Akaidi (Eds.), European Concurrent Engineering Conference. SCS Europe.
14. Herbst, J. (2001). Ein induktiver Ansatz zur Akquisition und Adaption von Workflow-Modellen. Ph.D. Thesis, Universität Ulm, November.
15. Herbst, J., & Karagiannis, D. (1998). Integrating machine learning and workflow management to support acquisition and adaptation of workflow models. In Proceedings of the Ninth International Workshop on Database and Expert Systems Applications (pp. 745-752). IEEE.
16. Herbst, J., & Karagiannis, D. (1999). An inductive approach to the acquisition and adaptation of workflow models. In M. Ibrahim, & B. Drabble (Eds.), Proceedings of the IJCAI'99 Workshop on Intelligent Workflow and Process Management: The New Frontier for AI in Business (pp. 52-57). Stockholm, Sweden, August.
17. J. Herbst D. Karagiannis 2000 Integrating machine learning and workflow management to support acquisition and adaptation of workflow models International Journal of Intelligent Systems in Accounting, Finance and Management 9 67 92
18. IDS Scheer (2002). ARIS Process Performance Manager (ARIS PPM). http://www.ids-scheer.com.
19. Kiepuszewski, B. (2003). Expressiveness and suitability of languages for control flow modelling in workflows. PhD Thesis, Queensland University of Technology, Brisbane, Australia. Available via http://www.workflowpatterns.com.
20. Mannila, H., & Rusakov, D. (2001). Decomposing event sequences into independent components. In V. Kumar, & R. Grossman (Eds.), Proceedings of the First SIAM Conference on Data Mining (pp. 1-17). SIAM.
21. H. Mannila H. Toivonen A. I. Verkamo 1997 Discovery of frequent episodes in event sequences Data Mining and Knowledge Discovery 1 3 259 289
22. Maruster, L., van der Aalst, W. M. P., Weijters, A. J. M. M., van den Bosch, A., & Daelemans, W. (2001). Automated discovery of workflow models from hospital data. In B. Kröse, M. de Rijke, G. Schreiber, & M. van Someren (Eds.), Proceedings of the 13th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 2001) (pp. 183-190).
23. Maruster, L., Weijters, A. J. M. M., van der Aalst, W. M. P., & van den Bosch, A. (2002). Process mining: Discovering direct successors in process logs. In Proceedings of the 5th International Conference on Discovery Science (Discovery Science 2002), Volume 2534 of Lecture Notes in Artificial Intelligence (pp. 364-373). Berlin: Springer.
24. Maxeiner, M. K., Küspert, K., & Leymann, F. (2001). Data Mining von Workflow-Protokollen zur teilautomatisierten Konstruktion von ProzeSSmodellen. In Proceedings of Datenbanksysteme in Büro, Technik und Wissenschaft (pp. 75-84). Berlin, Germany: Informatik Aktuell Springer.
25. Moreno, J. L. (1934). Who shall survive? Washington, DC: Nervous and Mental Disease Publishing Company.
26. Parekh, R., Honavar, V. (2000). Automata induction, grammar inference, and language acquisition. In Dale, Moisl, & Somers (Eds.), Handbook of Natural Language Processing. New York: Marcel Dekker.
27. Pitt, L. (1889). Inductive inference, DFAs, and computational complexity. In K. P. Jantke (Ed.), Proceedings of International Workshop on Analogical and Inductive Inference (AII), Volume 397 of Lecture Notes in Computer Science (pp. 18-44). Berlin: Springer.
28. Reisig, W., & Rozenberg, G. (Eds) (1998). Lectures on petri nets I: Basic models, Volume 1491 of Lecture Notes in Computer Science. Berlin: Springer.
29. Rozinat, A., & van der Aalst, W. M. P. (2006). Conformance testing: Measuring the fit and appropriateness of event logs and process models. In C. Bussler et al. (Eds.), BPM 2005 Workshop on Business Process Intelligence, Volume 3812 of Lecture Notes in Computer Science (pp. 163-176). Berlin: Springer.
30. Sayal, M., Casati, F., Shan, M. C., & Dayal, U. (2002). Business process cockpit. In Proceedings of 28th International Conference on Very Large Data Bases (VLDB'02) (pp. 880-883). Morgan Kaufmann.
31. Schimm, G. (2001). Process mining. http://www.processmining.de/.
32. Schimm, G. (2000). Generic linear business process modeling. In S. W. Liddle, H. C. Mayr, & Thalheim, B. (Eds.), Proceedings of the ER 2000 Workshop on Conceptual Approaches for E-Business and The World Wide Web and Conceptual Modeling, Volume 1921 of Lecture Notes in Computer Science (pp. 31-39). Berlin: Springer.
33. Schimm, G. (2002). Process miner-A tool for mining process schemes from event-based data. In S. Flesca, & G. Ianni (Eds.), Proceedings of the 8th European Conference on Artificial Intelligence (JELIA), Volume 2424 of Lecture Notes in Computer Science (pp. 525-528). Berlin: Springer.
34. Scott, J. (1992). Social network analysis. Sage, Newbury Park CA.
35. Staffware (2002). Staffware Process Monitor (SPM). http://www.staffware. com.
36. W. M. P. van der Aalst 1998 The application of Petri nets to workflow management The Journal of Circuits, Systems and Computers 8 1 21 66
37. van der Aalst, W. M. P., & Song, M. (2004). Mining social networks: Uncovering interaction patterns in business processes. In M. Weske, B. Pernici, & J. Desel (Eds.), International Conference on Business Process Management (BPM 2004), Lecture Notes in Computer Science. Berlin: Springer.
38. van der Aalst, W. M. P., & van Dongen, B. F. (2002). Discovering workflow performance models from timed logs. In Y. Han, S. Tai, & D. Wikarski (Eds.), International Conference on Engineering and Deployment of Cooperative Information Systems (EDCIS 2002), Volume 2480 of Lecture Notes in Computer Science (pp. 45-63). Berlin: Springer.
39. W. M. P. van der Aalst B. F. van Dongen J. Herbst L. Maruster G. Schimm A. J. M. M. Weijters 2003 Workflow mining: A survey of issues and approaches Data and Knowledge Engineering 47 2 237 267
40. van der Aalst, W. M. P., & van Hee, K. M. (2002). Workflow management: Models, methods, and systems. Cambridge, MA: MIT Press.
41. van der Aalst, W. M. P., Weijters, A. J. M. M. (Eds.), Process mining, special issue of computers in industry, Vol. 53, No. 3. Amsterdam: Elsevier (2004).
42. W. M. P. van der Aalst A. J. M. M. Weijters L. Maruster 2004 Workflow mining: Discovering process models from event logs IEEE Transactions on Knowledge and Data Engineering 16 9 1128 1142
43. van Dongen, B. F., Alves de Medeiros, A. K., Verbeek, H. M. W., Weijters, A. J. M. M., & van der Aalst, W. M. P. (2005). The prom framework: A new era in process mining tool support. In G. Ciardo, & P. Darondeau (Eds.), Application and Theory of Petri Nets 2005, Volume 3536 of Lecture Notes in Computer Science (pp. 444-454). Berlin: Springer.
44. Weijters, A. J. M. M., & van der Aalst, W. M. P. (2001a). Process mining: Discovering workflow models from event-based data. In B. Kröse, M. de Rijke, G. Schreiber, & M. van Someren (Eds.), Proceedings of the 13th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 2001) (pp. 283-290).
45. Weijters, A. J. M. M., & van der Aalst, W. M. P. (2001b). Rediscovering workflow models from event-based data. In V. Hoste, & G. de Pauw (Eds.), Proceedings of the 11th Dutch-Belgian Conference on Machine Learning (Benelearn 2001) (pp. 93-100).
46. Weijters, A. J. M. M., & van der Aalst, W. M. P. (2002). Workflow mining: Discovering workflow models from event-based data. In C. Dousson, F. Höppner, & R. Quiniou (Eds.), Proceedings of the ECAI Workshop on Knowledge Discovery and Spatial Data (pp. 78-84).
47. A. J. M. M. Weijters W. M. P. van der Aalst 2003 Rediscovering workflow models from event-based data using little thumb Integrated Computer-Aided Engineering 10 2 151 162
48. L. J. Wen W. M. P. van der Aalst J. M. Wang J. G. Sun 2007 Mining process models with non-free-choice constructs Data Mining and Knowledge Discovery 15 2 145 180
49. zur Mühlen, M. (2001a). Process-driven management information systems combining data warehouses and workflow technology. In B. Gavish (Ed.), Proceedings of the International Conference on Electronic Commerce Research (ICECR-4) (pp. 550-566). Los Alamitos, California: IEEE Computer Society Press.
50. zur Mühlen, M. (2001b). Workflow-based process controlling-Or: What you can measure you can control. In L. Fischer (Ed.), Workflow Handbook 2001, Workflow Management Coalition (pp. 61-77). Florida: Lighthouse Point, Future Strategies.
51. zur Mühlen, M., & Rosemann, M. (2000). Workflow-based process monitoring and controlling-technical and organizational issues. In R. Sprague (Ed.), Proceedings of the 33rd Hawaii International Conference on System Science (HICSS-33) (pp. 1-10). Los Alamitos, California: IEEE Computer Society Press.