/faculteit technologie management Workflow Mining: Current Status and Future Directions Ana Karla A. de Medeiros, W.M.P van der Aalst and A.J.M.M. Weijters Eindhoven University of Technology Department of Information and Technology

/faculteit technologie management Outline Motivation Workflow Mining:  -algorithm Workflow Mining: limitations Extensions to Mining Algorithms Discussion and Future work

/faculteit technologie management Workflow Mining – Motivation –Workflow Mining (What is the process?) –Delta analysis (Are we doing what was specified?) –Performance analysis (How can we improve?)

/faculteit technologie management Workflow Mining – Process log ABCDACBDEF 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 Minimal information in noise- free log: case id’s and task id’s Additional information: event type, time, resources, and data In this log there are three possible sequences:

/faculteit technologie management Workflow Mining – Ordering 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 Unrelated: 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... A>BA>CB>CB>DC>BC>DE>F ABABACACBDBDCDCDEFEFABABACACBDBDCDCDEFEF B||CC||BABCDACBDEF

/faculteit technologie management Workflow Mining –  -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 ).

/faculteit technologie management Workflow Mining –  -algorithmABCDACBDEF ABABACACBDBDCDCDEFEFABABACACBDBDCDCDEFEF B||CC||B

/faculteit technologie management Workflow Mining –  -algorithm If log is complete with respect to relation >, it can be used to mine SWF-net without short loops Structured Workflow Nets (SWF-nets) have no implicit places and the following two constructs cannot be used:

/faculteit technologie management Problematic constructs –Short loops –Invisible Tasks –Synchronization of OR-join places –Duplicate Tasks –Implicit Places –Non-free Choice Workflow Mining –  -algorithm limitations

/faculteit technologie management Problematic constructs –Short loops –Invisible Tasks –Synchronization of OR-join places –Duplicate Tasks –Implicit Places –Non-free Choice Workflow Mining –  -algorithm limitations

/faculteit technologie management  -algorithm limitations – Short Loops One-length Two-length B>B and not B>B implies BB (impossible!) A>B and B>A implies A||B and B||A instead of AB and BA

/faculteit technologie management Problematic constructs –Short loops –Invisible Tasks –Synchronization of OR-join places –Duplicate Tasks –Implicit Places –Non-free Choice Workflow Mining –  -algorithm limitations

/faculteit technologie management  -algorithm limitations – Invisible Tasks Nets are behaviorally equivalent!

/faculteit technologie management Problematic constructs –Short loops –Invisible Tasks –Synchronization of OR-join places –Duplicate Tasks –Implicit Places –Non-free Choice Workflow Mining –  -algorithm limitations

/faculteit technologie management  -algorithm limitations – Synchronization of OR-join places But...

/faculteit technologie management Problematic constructs –Short loops –Invisible Tasks –Synchronization of OR-join places –Duplicate Tasks –Implicit Places –Non-free Choice Workflow Mining –  -algorithm limitations

/faculteit technologie management  -algorithm limitations – Duplicate Tasks

/faculteit technologie management Problematic constructs –Short loops –Invisible Tasks –Synchronization of OR-join places –Duplicate Tasks –Implicit Places –Non-free Choice Workflow Mining –  -algorithm limitations

/faculteit technologie management  -algorithm limitations – Implicit Places

/faculteit technologie management Problematic constructs –Short loops –Invisible Tasks –Synchronization of OR-join places –Duplicate Tasks –Implicit Places –Non-free Choice Workflow Mining –  -algorithm limitations

/faculteit technologie management  -algorithm limitations – Non-free Choice But...

/faculteit technologie management Some problematic constructs relate because –Can have same complete workflow log and/or –Same set of ordering relations Workflow Mining – Trade-offs between Problematic Constructs XYXAAY X  A A  Y

/faculteit technologie management Process mining = 3-phase process 1.Pre-processing 2.Processing 3.Post-processing Example for 1-lenght loops in SWF-nets Workflow Mining – Extensions to Mining Algorithms

/faculteit technologie management Workflow Mining – Extensions to Mining Algorithms (Example)

/faculteit technologie management  -algorithm cannot mine correctly –Short loops –Invisible tasks –Duplicate Tasks –Implicit Places –Non-free Choice –Synchronization of OR-join Places Extensions are possible, but relations between problematic constructs imply in trade-offs Workflow Mining – Discussion and Future Work

/faculteit technologie management Future steps –Extend the class of WF-nets  -algorithm can correctly mine –Create mining algorithms to handle workflows beyond WF-nets –Develop mining heuristics to deal with noisy or incomplete workflow logs Workflow Mining – Discussion and Future Work

/faculteit technologie management Questions?