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/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 a.k.medeiros@tm.tue.nl
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/faculteit technologie management Outline Motivation Workflow Mining: -algorithm Workflow Mining: limitations Extensions to Mining Algorithms Discussion and Future work
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/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?)
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/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:
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/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 ABABACACBDBDCDCDEFEFABABACACBDBDCDCDEFEF B||CC||BABCDACBDEF
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/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 ).
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/faculteit technologie management Workflow Mining – -algorithmABCDACBDEF ABABACACBDBDCDCDEFEFABABACACBDBDCDCDEFEF B||CC||B
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/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:
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/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
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/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
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/faculteit technologie management -algorithm limitations – Short Loops One-length Two-length B>B and not B>B implies BB (impossible!) A>B and B>A implies A||B and B||A instead of AB and BA
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/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
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/faculteit technologie management -algorithm limitations – Invisible Tasks Nets are behaviorally equivalent!
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/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
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/faculteit technologie management -algorithm limitations – Synchronization of OR-join places But...
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/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
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/faculteit technologie management -algorithm limitations – Duplicate Tasks
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/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
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/faculteit technologie management -algorithm limitations – Implicit Places
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/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
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/faculteit technologie management -algorithm limitations – Non-free Choice But...
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/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
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/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
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/faculteit technologie management Workflow Mining – Extensions to Mining Algorithms (Example)
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/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
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/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
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/faculteit technologie management Questions?
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