A review of business process mining: State-of-the-art and future trends

Business Process Management Journal

Volumn 14, Issue 1, 2008, Pages 5-22

  Tiwari, A ^a   Turner, C J ^a   Majeed, B ^b  

^a CRANFIELD UNIVERSITY   (United Kingdom)

^b BRITISH TELECOM   (United Kingdom)

Author keywords

Computer applications; Process management; Work flow

Indexed keywords

EID: 38949216693     PISSN: 14637154     EISSN: None     Source Type: Journal    
DOI: 10.1108/14637150810849373     Document Type: Review

References (46)

1. Adams, N.M., Hand, D.J. and Till, R.J. (2001), “Mining for classes and patterns in behavioural data”, Journal of the Operational Research Society, Vol. 52, pp. 1017-24.
2. Agrawal, R., Gunopulos, D. and Leymann, F. (1998), “Mining process models from workflow logs”, in Schek, H.J. (Ed.), Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology, Springer Verlag, Heidelberg.
3. Aguilar-Saven, R.S. (2004), “Business process modelling: review and framework”, International Journal of Production Economics, Vol. 90, pp. 129-49.
4. Alves de Medeiros, A.K., van der Aalst, W.M.P. and Weijters, A.J.M.M. (2003), “Workflow mining: current status and future directions”, in Meersman, R. et al. (Eds), CoopIS/DOA/ODBASE 2003, Springer Verlag, Heidelberg, pp. 389-406.
5. Alves de Medeiros, A.K., Weijters, A.J.M.M. and van der Aalst, W.M.P. (2004a), “Using genetic algorithms to mine process models: representation, operators and results”, Beta Working Paper Series, WP 124, Eindhoven University of Technology, Eindhoven.
6. Alves de Medeiros, A.K., Weijters, A.J.M.M. and van der Aalst, W.M.P. (2005), “Genetic process mining: a basic approach and its challenges”, in Bussler, C. and Haller, A. (Eds), Business Process Management Workshops: BPM 2005, Springer Verlag, Heidelberg.
7. Alves de Medeiros, A.K., van Dongen, B.F., van der Aalst, W.M.P. and Weijters, A.J.M.M. (2004b), “Process mining: extending the α-algorithm to mine short loops”, Beta Working Paper Series, WP 113, Eindhoven University of Technology, Eindhoven.
8. Alves de Medeiros, A.K., van Dongen, B.F., van der Aalst, W.M.P. and Weijters, A.J.M.M. (2004c), “Process mining for ubiquitous mobile systems: an overview and a concrete algorithm”, in Baresi, L., Dustdar, S., Gall, H. and Materaseries, M. (Eds), Ubiquitous Mobile Information and Collaboration Systems (UMICS 2004), Springer Verlag, Heidelberg.
9. Chen, K.C.W. and Yun, D.Y.Y. (2003), “Discovering process models from execution history by graph matching”, in Liu, J. et al. (Eds), IDEAL 2003, Springer Verlag, Heidelberg, pp. 887-92.
10. Cook, J.E. and Wolf, A.L. (1998a), “Discovering models of software processes from event-based data”, ACM Transactions on Software Engineering and Methodology, Vol. 7 No. 3, pp. 215-49.
11. Cook, J.E. and Wolf, A.L. (1998b), “Event-based detection of concurrency”, Proceedings of the 6th International Symposium on the Foundations of Software Engineering, ACM Press, New York, NY.
12. Cook, J.E., Du, Z., Liu, C. and Wolf, A.L. (2004), “Discovering models of behaviour for concurrent workflows”, Computers in Industry, Vol. 53, pp. 297-319.
13. Dong, G. and Li, J. (1999), “Efficient mining of emerging patterns: discovering trends and differences”, International Conference on Knowledge Discovery and Data Mining, ACM Press, New York, NY.
14. Dongen, B.F. and van der Aalst, W.M.P. (2004a), “EMIT: a process mining tool”, in Cortadelle, J. and Reisig, W. (Eds), 25th International Conference on Applications and Theory of Petri Nets (ICATPN 2004), Springer Verlag, Heidelberg.
15. Dongen, B.F. and van der Aalst, W.M.P. (2004b), “Multi-phase process mining: building instance graphs”, in Atzeni, P., Chu, W., Lu, H., Zhou, S. and Ling, T.W. (Eds), Conceptual Modelling – ER 2004, Springer Verlag, Heidelberg.
16. Dongen, B.F. and van der Aalst, W.M.P. (2005a), “A meta model for process mining data”, Proceedings of the CAiSE'05 Workshops, Vol. 2, pp. 309-20.
17. Dongen, B.F. and van der Aalst, W.M.P. (2005b), “Multi-phase process mining: aggregating instance graphs into EPCs and Petri nets”, paper presented at 2nd International Workshop on Applications of Petri Nets to Coordination, Workflow and Business Process Management, at the ICATPN, Miami, FL, June.
18. Dongen, B.F., van der Aalst, W.M.P. and Verbeek, H.M.W. (2005a), “Verification of EPCs: using reduction rules and Petri nets”, in Pastor, O. and Cunha, J.F. (Eds), 17th International Conference on Advanced Information Systems Engineering (CAiSE 2005), Springer Verlag, Heidelberg, pp. 372-86.
19. Dongen, B.F., Alves de Medeiros, A.K., Verbeek, H.M.W., Weijters, A.J.M.M. and van der Aalst, W.M.P. (2005b), “The ProM framework: a new era in process mining tool support”, in Ciardo, G. and Darondeau, P. (Eds), 26th International Conference on Applications and Theory of Petri Nets (ICATPN 2005), Springer Verlag, Heidelberg.
20. Dustdar, S., Hoffmann, T. and van der Aalst, W. (2004), “Mining of ad-hoc business processes with TeamLog”, Technical Report TUV-1841-2004-07, Technical University of Vienna, Vienna.
21. Gaaloul, W. and Godart, C. (2005), “Mining workflow recovery from event based logs”, in van der Aalst, W.M.P. et al. (Eds), BPM 2005, Springer Verlag, Heidelberg.
22. Golani, M. and Pinter, S. (2003), “Generating a process model from a process audit log”, in van der Aalst, W.M.P. (Ed.), Business Process Management, Springer Verlag, Heidelberg.
23. Greco, G., Guzzo, A., Manco, G. and Sacca, D. (2005), “Mining and reasoning on workflows”, IEEE Transactions on Knowledge and Data Engineering, Vol. 17 No. 4, pp. 519-34.
24. Greco, G., Guzzo, A., Pontieri, L. and Sacca, D. (2004), “Mining expressive process models by clustering workflow traces”, in Dai, H., Srikant, R. and Zhang, C. (Eds), PAKDD 2004, Springer Verlag, Heidelberg, pp. 52-62.
25. Hammori, M., Herbst, J. and Kleiner, N. (2004), “Interactive workflow mining”, in Desel, B.P. and Weske, M. (Eds), Business Process Management, Springer Verlag, Heidelberg, pp. 211-66.
26. Herbst, J. and Karagiannis, D. (1998), “Integrating machine learning and workflow management to support acquisition and adaptation of workflow models”, Proceedings of the 9th International Workshop on Database and Expert Systems Applications, IEEE Computer Society, Washington, DC.
27. Herbst, J. and Karagiannis, D. (2004), “Workflow mining with InWoLvE”, Computers in Industry, Vol. 53, pp. 245-64.
28. Hwang, S.Y. (2002), “On the discovery of process models from their instances”, Decision Support Systems, Vol. 34, pp. 41-57.
29. Hwang, S.Y., Wei, C.P. and Yang, W.S. (2004), “Discovery of temporal patterns from process instances”, Computers in Industry, Vol. 53, pp. 345-64.
30. Mannila, H. and Rusakov, D. (2001), “Decomposition of event sequences into independent components”, Proceedings of the First SIAM International Conference on Data Mining, Chicago, 5-7 April.
31. Maruster, L., Weijters, A.J.M.M., van der Aalst, W.M.P. and van den Bosch, A. (2002a), “Process mining: discovering direct successors in process logs”, Proceedings of the 5th International Conference on Discovery Science (Discovery Science 2002), Springer Verlag, Berlin.
32. Maruster, L., van der Aalst, W.M.P., Weijters, T., van den Bosch, A. and Daelemans, W. (2002b), “Automated discovery of workflow logs from hospital data”, in Dousson, C., Höppner, F. and Quiniou, R. (Eds), Proceedings of the ECAI Workshop on Knowledge Discovery and Spatial Data, pp. 78-84.
33. Schimm, G. (2003), “Mining most specific workflow models from event-based data”, in van der Aalst, W.M.P. (Ed.), Business Process Management, Springer Verlag, Heidelberg.
34. Schimm, G. (2004), “Mining exact models of concurrent workflows”, Computers in Industry, Vol. 53, pp. 265-81.
35. van der Aalst, W.M.P. (2003), “Workflow mining: a survey of issues and approaches”, Data & Knowledge Engineering, Vol. 47, pp. 237-67.
36. van der Aalst, W.M.P. (2004a), “Process mining: a research agenda”, Computers in Industry, Vol. 53, pp. 231-44.
37. van der Aalst, W.M.P. (2004b), “Workflow mining: discovering process models from event logs”, IEEE Transactions on Knowledge and Data Engineering, Vol. 16 No. 9, pp. 1128-42.
38. van der Aalst, W.M.P. (2005), “Business alignment: using process mining as a tool for delta analysis and conformance testing”, Requirements Engineering Journal, Vol. 10 No. 3, pp. 198-211.
39. van der Aalst, W.M.P. and Alves de Medeiros, A.K. (2005), “Process mining and security: detecting process executions and checking process conformance”, Electronic Notes in Theoretical Computer Science, Vol. 121, pp. 3-21.
40. van der Aalst, W.M.P. and Song, M. (2004), “Mining social networks: uncovering interaction patterns in business processes”, in Desel, J., Pernici, B. and Weske, M. (Eds), International Conference on Business Process Management (BPM 2004), Springer Verlag, Berlin.
41. van der Aalst, W.M.P., Alves de Medeiros, A.K. and Weijters, A.J.M.M. (2005a), “Genetic process mining”, in Ciardo, G. (Ed.), Applications and Theory of Petri Nets, Springer Verlag, Heidelberg.
42. van der Aalst, W.M.P., de Beer, H.T. and van Dongen, B.F. (2005b), “Process mining and verification of properties: an approach based on temporal logic”, in Meersman, R. and Tari, Z. (Eds), On the Move to Meaningful Internet Systems 2005, Springer Verlag, Heidelberg.
43. van der Aalst, W.M.P., Weijters, A.J.M.M. and Maruster, L. (2002), “Workflow mining: which processes can be rediscovered?”, Beta Working Paper Series, WP 75, Eindhoven University of Technology, Eindhoven.
44. Weijters, A.J.M.M. and van der Aalst, W.M.P. (2001), “Process mining: discovering workflow models from event based data”, in Kröse, B., Rijke, M., Schreiber, G. and Someren, M. (Eds), Proceedings of the 13th Belgium-Netherlands Conference on Artificial Intelligence, Amsterdam, October.
45. Weijters, A.J.M.M. and van der Aalst, W.M.P. (2003), “Rediscovering workflow models from event-based data using little thumb”, Integral Computer-aided Engineering, Vol. 10 No. 2, pp. 151-62.
46. Zhang, S.H., Gu, N., Lian, J.X. and Li, S.H. (2003), “Workflow process mining based on machine learning”, Proceedings of the Second International Conference on Machine Learning and Cybernetics, IEEE Computer Society, Washington, DC.