1. Process Mining in CSCW Systems All truths are easy to understand once they are discovered; the point is to discover them. Galileo Galilei (1564 - 1642) Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department of Information Systems P.O. Box 513, 5600 MB Eindhoven The Netherlands w.m.p.v.d.aalst@tm.tue.nl

2. Outline CSCW spectrum –spectrum –motivation Process Mining –overview –a concrete algorithm: the alpha algorithm ProM –Architecture –Convertors (e-mail, Staffware, InConcert, SAP, etc.) –Process mining plug-ins –Analysis plug-ins –Conformance testing plug-in –LTL checker plug-in –Social network plug-in Conclusion

3. CSCW spectrum we want to apply process mining to each of these domains...

4. process mining is relevant across the whole spectrum

5. Motivation BAM (Business Activity Monitoring), BOM (Business Operations Management), BPI (Business Process Intelligence) illustrate the interest in process improvement based on monitoring data. Systems need to be adaptive and self-managing thus increasing the need for monitoring. Legislation such as the Sarbanes-Oxley Act, is forcing organizations to monitor activities and processes. Technology push: The data is there and more will come! (cf. ERP systems, RFID, webservices, etc.).

6. Process Mining

8. Motivation: Reversing the process 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?) process mining

9. Overview 1) basic performance metrics 2) process model3) organizational model4) social network 5) performance characteristics If …then … 6) auditing/security www.processmining.org

10. Let us focus on mining process models … 1) basic performance metrics 2) process model3) organizational model4) social network 5) performance characteristics If …then … 6) auditing/security... and a very simple approach: The alpha algorithm

11. Alpha algorithm α

12. 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 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

13. >, ,||,# 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. 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>B A>C B>C B>D C>B C>D E>F ABACBDCDEFABACBDCDEF B||C C||B

14. Basic idea (1) xyxy

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

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

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

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

19. It is not that simple: Basic alpha algorithm Let W be a workflow log over T.  (W) is defined as follows. 1.T W = { t  T     W t   }, 2.T I = { t  T     W t = first(  ) }, 3.T O = { t  T     W t = last(  ) }, 4.X W = { (A,B)  A  T W  B  T W   a  A  b  B a  W b   a1,a2  A a 1 # W a 2   b1,b2  B b 1 # W b 2 }, 5.Y W = { (A,B)  X   (A,B)  X A  A  B  B  (A,B) = (A,B) }, 6.P W = { p (A,B)  (A,B)  Y W }  {i W,o W }, 7.F W = { (a,p (A,B) )  (A,B)  Y W  a  A }  { (p (A,B),b)  (A,B)  Y W  b  B }  { (i W,t)  t  T I }  { (t,o W )  t  T O }, and  (W) = (P W,T W,F W ). The alpha algorithm has been proven to be correct for a large class of free-choice nets.

20. Example 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  (W) W

21. DEMO Alpha algorithm 48 cases 16 performers

22. ProM framework

23. ProM

24. Converter plug-in: EMailAnalyzer

25. XML format

26. ProM architecture

27. Mining plug-in: Alpha algorithm

28. Mining plug-in: Genetic Miner

29. Approach

30. Mining plug-in: Multi-phase mining

31. Step 1: Get instances

32. Step 2: Project

33. Step 3: Aggregate

34. Step 4: Map onto EPC

35. Step 5: Map onto Petri net (or other language)

36. Mining plug-in: Social network miner

38. Cliques

39. SN based on hand-over of work metric density of network is 0.225

40. SN based on working together (and ego network)

41. Analysis plug-in: LTL checker

43. Analysis plug-in: Conformance checker Do they agree?

45. Fitness is not enough

46. Screenshot (Also runs on Mac.)

47. Other analysis plug-ins

48. More demos?

49. Conclusion Process mining provides many interesting challenges for scientists, customers, users, managers, consultants, and tool developers. Interesting across the whole CSCW spectrum. Get ProM-ed! You can contribute by applying ProM and developing plug-ins.

50. Thanks to Ton Weijters, Boudewijn van Dongen, Ana Karla Alves de Medeiros, Minseok Song, Laura Maruster, Eric Verbeek, Monique Jansen-Vullers, Hajo Reijers, Michael Rosemann, Anne Rozinat, Christian Guenther Peter van den Brand, Huub de Beer, Andrey Nikolov, et al. for their on-going work on process mining.

51. More information http://www.workflowcourse.com http://www.workflowpatterns.com http://www.processmining.org BPM 2005, Sept., Nancy France http://bpm2005.loria.fr/