1. Process Mining: Discovering processes from event logs 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 and Technology P.O. Box 513, 5600 MB Eindhoven The Netherlands w.m.p.v.d.aalst@tm.tue.nl

2. Outline BPM research in Eindhoven Process Mining ProM –Architecture –Process mining plug-ins Alpha-algorithm Multi-phase mining Genetic mining –Analysis plug-ins –Conformance testing plug-in –LTL checker plug-in –Social network plug-in –Convertors (e-mail, Staffware, InConcert, SAP, etc.) Conclusion

3. BPM research in Eindhoven

4. Computer Science/information Systems in Eindhoven TU/e TM (Technology Management) MCS (Math. and Comp. Science) IS (Information Systems) CS (Computer Science) BPM (Business Process Management) Math. ICTA SE IS (Information Systems) BETA

5. Research within the BPM group Workflow management systems Process modeling (design and redesign) Process analysis (validation, verification, and performance analysis) Process mining Often based on Petri nets

6. Analysis and improvement of resource-constrained workflow processes Mariska Netjes

7. Product-driven process design Irene Vanderfeesten

8. Improving work distribution mechanisms in WFM systems Maja Pesic

9. Patterns in process-aware information systems Nataliya Mulyar

10. Genetic process mining Ana Karla Alves de Medeiros

11. Process mining in case handling systems Christian Günther

12. Process mining: a formal approach Boudwijn van Dongen

13. Verification of workflow processes Eric Verbeek

14. YAWL: Yet Another Workflow Language

15. Arthur ter Hofstede (patterns, YAWL def.) Marlon Dumas (BABEL, SOC) Lachlan Aldred (YAWL engine, comm. pats) Lindsay Bradford (YAWL designer) Tore Fjellheim (YAWL logging, timers) Guy Redding (YAWL forms) Nick Russell (work distribution/transactions) Moe Wynn (verification, optimization) Michael Adams (flexibility)

16. Process Mining

17. 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

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

19. 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

20. 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

21. >, ,||,# 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

22. Basic idea (1) xyxy

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

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

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

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

27. 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.

28. 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

29. Challenges Refining existing algorithm for (control-flow/process perspective) –Hidden tasks –Duplicate tasks –Non-free-choice constructs –Loops –Detecting concurrency (implicit or explicit) –Mining and exploiting time –Dealing with noise –Dealing with incompleteness Mining other perspectives (data, resources, roles, …) Gathering data from heterogeneous sources Visualization of results Delta analysis

30. DEMO Alpha algorithm 48 cases 16 performers

31. ProM framework

32. ProM

33. Converter plug-in: EMailAnalyzer

34. XML format

35. ProM architecture

36. Mining plug-in: Multi-phase mining

37. Step 1: Get instances

38. Step 2: Project

39. Step 3: Aggregate

40. Step 4: Map onto EPC

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

42. Mining plug-in: Genetic Miner

43. Overview of approach

44. Analysis plug-in: Social network miner

46. Cliques

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

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

49. Analysis plug-in: LTL checker

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

53. Fitness is not enough

54. Screenshot (Also runs on Mac.)

55. Other analysis plug-ins

56. More demos?

57. Conclusion Process mining provides many interesting challenges for scientists, customers, users, managers, consultants, and tool developers. Involves multiple perspectives (process, data, resources, etc.) Get ProM-ed! You can contribute by applying ProM and developing plug-ins

58. More information http://www.workflowcourse.com http://www.workflowpatterns.com http://www.processmining.org