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Process Mining: The next step in Business Process Management

Process Mining: The next step in Business Process Management. Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department of Information and Technology P.O. Box 513, 5600 MB Eindhoven The Netherlands w.m.p.v.d.aalst@tm.tue.nl &

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Process Mining: The next step in Business Process Management

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  1. Process Mining: The next step in Business Process Management Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department of Information and Technology P.O. Box 513, 5600 MB Eindhoven The Netherlands w.m.p.v.d.aalst@tm.tue.nl & Centre for Information Technology Innovation (CITI) Queensland University of Technology (QUT) Brisbane, Australia

  2. Outline • Motivation • Overview of process mining • Basic performance metrics • Process models • Organizational models • Social networks • Performance characteristics • Process Mining: Some of our tools • EMiT • Thumb • MinSocN • Conclusion

  3. Workflow/BPM in The Netherlands • “The Netherlands in the country with the highest density of workflow systems per capita” John O'Connell (CEO Staffware)(cf. population density per sq. km390 versus 2.5 for Australia) • Emphasis on process modelingand analysis (the European way) • Innovative companies like PallasAthena, Baan, …

  4. I&T department, Eindhoven University of Technology • Embedded in research institute BETA joining multiple disciplines • Three subgroups: • Business Process Management(workflow management, Petri nets, mining, ...) • ICT Architectures(agents, transactions, ...) • Software Engineering(software quality, ...) • Team working on process mining: Wil van der Aalst, Ton Weijters, Ana Karla Alves de Medeiros, Boudewijn van Dongen, Eric Verbeek, Minseok Song, Monique Vullers-Jansen, Laura Maruster, …

  5. Motivation

  6. (Zur Muehlen 2003) 25 years of workflow • Pioneers like Skip Ellis and Michael Zisman already worked on “office automation” in the 70-ties • The WFM hype is over …, but there are more and more applications, it has become a mature technology, and WFM is adopted by many other technologies (ERP, Web Services, etc.).

  7. Let us reverse the process! process mining • Process mining can be used for: • Process discovery (What is the process?) • Delta analysis (Are we doing what was specified?) • Performance analysis (How can we improve?) • Particularly interesting in pre- and post-workflow settings!

  8. Process mining: Overview

  9. Classification of process mining The following types of process mining can be distinguished: • Determine basic performance metrics • Determine process model • Determine organizational model • Analyze social network (i.e., relations between actors) • Analyze performance characteristics (i.e., derive rules explaining performance)

  10. 2) process model 3) organizational model 4) social network 1) basic performance metrics 5) performance characteristics If …then …

  11. (1) Determine basic performance metrics • Process/control-flow perspective: flow time, waiting time, processing time and synchronization time.Questions: • What is the average flow time of orders? • What is the maximum waiting time for activity approve? • What percentage of requests is handled within 10 days? • What is the minimum processing time of activity reject? • What is the average time between scheduling an activity and actually starting it? • Resource perspective: frequencies, time, utilization, and variability.Questions: • How many times did Sue complete activity reject claim? • How many times did John withdraw activity go shopping? • How many times did Clare suspend some running activity? • How much time did Peter work on instances of activity reject claim? • How much time did people with role Manager work on this process? • What is the utilization of John? • What is the average utilization of people with role Manager? • How many times did John work for more than 2 hours without interruption?

  12. Example (ARIS PPM) IDS Scheer's ARIS Process Performance Manager

  13. (2) Determine process model • Discover a process model (e.g., in terms of a PN or EPC) without prior knowledge about the structure of the process. case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D a(W)

  14. (3) Determine organizational model • Discover the organizational model (i.e., roles, departments,etc.) without prior knowledge about the structure of the organization. e.g., correspondence analysis (typically applied in ecology)

  15. (4) Analyze social network • Social Network Analysis (SNA) • Based on: • Handover of work • Subcontracting • Working together • Reassignments • Doing similar tasks

  16. Example

  17. (5) Analyze performance characteristics • Each case (process/workflow instance) has a number of properties: • Resource that worked on a specific activity • Value of a characteristic data element (e.g., size of order, age of customer, etc.) • Performance metrics of case (e.g., flow time) • Using machine-learning techniques it is possible to find relevant relations between these properties.

  18. Example • If John and Mike work together, it takes longer. • Expensive cases require less processing. • Etc.

  19. Process mining: The tools • EMiT • Thumb • MinSocN

  20. Process Mining: Tooling

  21. Example: processing customer orders Example in Staffware: 7 tasks and all basic routing constructs

  22. Fragment of Staffware log Case 21 Diractive Description Event User yyyy/mm/dd hh:mm ---------------------------------------------------------------------------- Start swdemo@staffw_edl 2003/02/05 15:00 Register order Processed To swdemo@staffw_edl 2003/02/05 15:00 Register order Released By swdemo@staffw_edl 2003/02/05 15:00 Prepare shipment Processed To swdemo@staffw_edl 2003/02/05 15:00 (Re)send bill Processed To swdemo@staffw_edl 2003/02/05 15:00 (Re)send bill Released By swdemo@staffw_edl 2003/02/05 15:01 Receive payment Processed To swdemo@staffw_edl 2003/02/05 15:01 Prepare shipment Released By swdemo@staffw_edl 2003/02/05 15:01 Ship goods Processed To swdemo@staffw_edl 2003/02/05 15:01 Ship goods Released By swdemo@staffw_edl 2003/02/05 15:02 Receive payment Released By swdemo@staffw_edl 2003/02/05 15:02 Archive order Processed To swdemo@staffw_edl 2003/02/05 15:02 Archive order Released By swdemo@staffw_edl 2003/02/05 15:02 Terminated 2003/02/05 15:02 Case 22 Diractive Description Event User yyyy/mm/dd hh:mm ---------------------------------------------------------------------------- Start swdemo@staffw_edl 2003/02/05 15:02 Register order Processed To swdemo@staffw_edl 2003/02/05 15:02 Register order Released By swdemo@staffw_edl 2003/02/05 15:02 Prepare shipment Processed To swdemo@staffw_edl 2003/02/05 15:02

  23. Fragment of XML file <?xml version="1.0"?> <!DOCTYPE WorkFlow_log SYSTEM "http://www.tm.tue.nl/it/research/workflow/mining/WorkFlow_log.dtd"> <WorkFlow_log> <source program="staffware"/> <process id="main_process"> <case id="case_0"> <log_line> <task_name>Case start</task_name> <event kind="normal"/> <date>05-02-2003</date> <time>15:04</time> </log_line> <log_line> <task_name>Register order</task_name> <event kind="schedule"/> <date>05-02-2003</date> <time>15:04</time>

  24. EMiT Focus on time.

  25. Thumb Focus on noise.

  26. Thumb is able to deal with noise (D/F-graphs) 10% noise no noise causality

  27. Representation in terms of an EPC…(collaboration with IDS Scheer)

  28. MinSocN (Mining Social Networks)

  29. Real case: CJIB • Processing of fines • 130136 cases • 99 different activities

  30. Process in EMiT

  31. Complete process model Validated by CJIB

  32. Conclusion

  33. Conclusion (1) • Process mining is practically relevant and the logical next step in Business Process Management.

  34. Conclusion (2) • Process mining provides many interesting challenges for scientists, customers, users, managers, consultants, and tool developers. 2) process model 3) organizational model 4) social network 1) basic performance metrics 5) performance characteristics If …then …

  35. More information • http://www.tm.tue.nl/it/research/workflow_mining.htm • http://www.tm.tue.nl/it/research/patterns • http://www.tm.tue.nl/it/staff/wvdaalst • W.M.P. van der Aalst and K.M. van Hee. Workflow Management: Models, Methods, and Systems. MIT press, Cambridge, MA, 2002.

  36. References BPM (just books and far from complete) • W.M.P. van der Aalst and K.M. van Hee. Workflow Management: Models, Methods, and Systems. MIT press, Cambridge, MA, 2002. • Workflow Management: Modeling Concepts, Architecture and Implementation by Stefan Jablonski and Christoph Bussler; Paperback: 351 pages; International Thomson Publishing, October 1996. • Production Workflow: Concepts and Techniques, by Frank Leymann, Dieter Roller, Andreas Reuter; Paperback, 479 pages; Prentice Hall PTR, 1st edition, September 1999. • Workflow-Based Process Controlling: Foundation, Design and Application of Workflow-Driven Process Information Systems, by Michael Zur Muehlen. Logos, Berlin, 2003 • Proceedings of the International Conference on Business Process Management (BPM), Eindhoven, The Netherlands, June 26-27, 2003, by Wil M. P. van der Aalst, Arthur H. M. ter Hofstede, and Mathias Weske (Editors); Paperback, 391 pages; Springer Verlag, 2003. • W.M.P. van der Aalst, J. Desel, and A. Oberweis, editors. Business Process Management: Models, Techniques, and Empirical Studies, volume 1806 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, 2000.

  37. References (2) • Internet Based Workflow Management: Towards a Semantic Web by Dan C. Marinescu; Hardcover, 626 pages; John Wiley & Sons, 1st edition, April 2002. • Web Services, by Gustavo Alonso, Fabio Casati, Harumi Kuno, and Vijay Machiraju; Hardcover, 480 pages, Springer Verlag, June 2003. • The Workflow Imperative, by Thomas M. Koulopolous; Hardcover, 240 pages; Van Nostrand Reinhold, 1st edition, January 1995. • Database Support for Workflow Management: The WIDE Project, by Paul Grefen, Barbara Pernici, and Gabriel Sanchez (Editors); Hardcover, 296 pages. Kluwer Academic Publishers, February, 1999. • Design and Control of Workflow Processes: Business Process Management for the Service Industry (Lecture Notes in Computer Science # 2617), by Hajo Reijers; Paperback, 320 pages; Springer Verlag; October 2003. • Practical Workflow for SAP - Effective Business Processes using SAP's WebFlow Engine, by Alan Rickayzen et al; Hardcover, 52 pages; SAP Press, July 2002. • Workflow Modeling: Tools for Process Improvement and Application Development, by Alec Sharp and Patrick McDermott, Hardcover, 345 pages; Artech House, 1st edition, February 2001. • Business Process Modelling With ARIS: A Practical Guide, by Rob Davis; Paperback, 545 ; Springer Verlag, August 2001.

  38. References (3) • Workflow Handbook 2003, by Layna Fischer (Editor); Hardcover, 384 pages. Future Strategies, April 2003. Specific for process mining: • W.M.P. van der Aalst, B.F. van Dongen, J. Herbst, L. Maruster, G. Schimm, and A.J.M.M. Weijters. Workflow Mining: A Survey of Issues and Approaches. Data and Knowledge Engineering , 47(2):237-267, 2003. • W.M.P. van der Aalst and B.F. van Dongen. Discovering Workflow Performance Models from Timed Logs. EDCIS 2002, volume 2480 of Lecture Notes in Computer Science, pages 45-63. Springer-Verlag, Berlin, 2002. • A.J.M.M. Weijters and W.M.P. van der Aalst. Rediscovering Workflow Models from Event-Based Data using Little Thumb. Integrated Computer-Aided Engineering, 10(2):151-162, 2003. • W.M.P. van der Aalst and A.J.M.M. Weijters, editors. Process Mining, Special Issue of Computers in Industry, Elsevier Science Publishers, Amsterdam, 2004. • W.M.P. van der Aalst, A.J.M.M. Weijters, and L. Maruster. Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering (to appear).

  39. Appendix: A concrete algorithm

  40. Process Mining: The alpha algorithm alpha algorithm

  41. case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D Process log • Minimal information in log: case id’s and task id’s. • Additional information: event type, time, resources, and data. • In this log there are three possible sequences: • ABCD • ACBD • EF

  42. A>B A>C B>C B>D C>B C>D E>F case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D >,,||,# relations • Direct succession: x>y iff for some case x is directly followed by y. • Causality: xy iff x>y and not y>x. • Parallel: x||y iff x>y and y>x • Choice: x#y iff not x>y and not y>x. B||C C||B AB AC BD CD EF

  43. Basic idea (1) xy

  44. Basic idea (2) xy, xz, and y||z

  45. Basic idea (3) xy, xz, and y#z

  46. Basic idea (4) xz, yz, and x||y

  47. Basic idea (5) xz, yz, and x#y

  48. It is not that simple: Basic alpha algorithm • Let W be a workflow log over T. a(W) is defined as follows. • TW = { t Î T  |$sÎ W t Îs}, • TI = { t Î T  |$sÎ W t = first(s) }, • TO = { t Î T  |$sÎ W t = last(s) }, • XW = { (A,B) |  A Í TWÙ B Í TWÙ"a Î A"b Î B a ®W b   Ù"a1,a2 Î A a1#W a2Ù"b1,b2 Î B b1#W b2 }, • YW = { (A,B) Î X  |"(A¢,B¢) Î XA Í A¢ÙB Í B¢Þ (A,B) = (A¢,B¢) }, • PW = { p(A,B)|  (A,B) Î YW } È{iW,oW}, • FW = { (a,p(A,B))  |  (A,B) Î YWÙ a Î A }  È { (p(A,B),b)  |  (A,B) Î YWÙ b Î B }  È{ (iW,t)  |  t Î TI}  È{ (t,oW)  | t Î TO}, and • a(W) = (PW,TW,FW).

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