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This presentation was given by Dirk Fahland at the International Conference on Business Process Management 2011 (BPM'11) in Clermont-Ferrand, France on 31st August 2011.

This presentation was given by Dirk Fahland at the International Conference on Business Process Management 2011 (BPM'11) in Clermont-Ferrand, France on 31st August 2011.

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Simplifying Mined Process Models Presentation Transcript

  • 1. Dirk Fahland Wil M.P. van der Aalst SimplifyingMined Process Models
  • 2. Process Mining, Currentlyevent process mining process log algorithm model PAGE 1
  • 3. Process Mining, Currently readableevent process mining process process log algorithm model model PAGE 2
  • 4. Post-Process the Model readable event process mining process simplify process log algorithm model modelcan replay the entire log introduce: post-processing can replay operations on the the entire log mined model PAGE 3
  • 5. …Based on Original Event Log process mining process algorithm model readable event simplify process log modelcan replay the entire log introduce: post-processing can replay operations on the the entire log mined model PAGE 4
  • 6. Analysis process mining process algorithm model readable event simplify process log model discover ordering relations  infer behavior behavior observed executions generalized behaviorincomplete knowledge PAGE 5
  • 7. Idea: Re-Adjust Generalization process mining process algorithm model readable event simplify process log model unfold model wrt. log modelcomplexity fold, simplify, generalize behavior log PAGE 6
  • 8. Unfold a Spaghetti-Model PAGE 7
  • 9. Unfold Model wrt. a Log A ABDA ABCBDA ABCBC log C B D mined process model PAGE 8
  • 10. Unfold Model wrt. a Log unfold A A ABDA ABCBDA B ABCBC log C B D A D mined process model PAGE 9
  • 11. Unfold Model wrt. a Log unfold A A ABDA ABCBDA B ABCBC log C B C D B A D mined D process model A PAGE 10
  • 12. Unfold Model wrt. a Log unfold A A ABDA ABCBDA B ABCBC log C B C D B A D mined D process modelC A PAGE 11
  • 13. Unfold Model wrt. a Log unfold A A ABDA ABCBDA B ABCBC log C B C D B A D mined D B process modelCB A unfolding wrt. the log PAGE 12
  • 14. Represents Concurrency unfold A A AEBDA ABECBDA B E ABCBC log C B E C D B A D mined D process modelC A unfolding wrt. the log PAGE 13
  • 15. Represents Concurrency A AEBDA ABECBDA B E ABCBC log C D • is a process model B A • contains only behavior in the log • is acyclicC D • represents concurrency explicitly • labeled (several tasks with same label) A unfolding wrt. the log PAGE 14
  • 16. Represents Concurrency A AEBDA ABECBDA B E ABCBC log C D unfold B A fold, simplify,C D generalize A unfolding wrt. the log PAGE 15
  • 17. Fold an unfolded model A merge equivalent nodes B E necessary condition on equivalent transitions C D • same label B AC D A PAGE 16
  • 18. Fold an unfolded model A merge equivalent nodes B E necessary condition on equivalent transitions C D • same label • equivalent pre-/post-places B AC D A PAGE 17
  • 19. Fold an unfolded model A merge equivalent nodes B E necessary condition on equivalent transitions C D • same label • equivalent pre-/post-places B A various equivalences possible (see paper for some)C D A PAGE 18
  • 20. Fold an unfolded model A merge equivalent nodes B E A C D C B E B A DC D A A PAGE 19
  • 21. Unfolding and Refolding unfold A fold A C B E C B E D D refolded vs. original model • less behavior (replays the log and more) A • simpler structure PAGE 20
  • 22. Next: Simplifying and Generalizing readable process simplify simplify process model model unfoldcomplexity fold simplify, generalize behavior log PAGE 21
  • 23. Implied Places A implied place • does not restrict transitions B fold A remove from folded model C D • simpler model C B • same behavior B A D various techniques to findC D implied places A A PAGE 22
  • 24. Special: Implied Places and Folding A Ap p A B C D C p fold D B C unfolding wrt. log folding may merge implied and non-implied places remove p: simpler model, more behavior (generalization) let user decide PAGE 23
  • 25. Configurable Simplification readable process simplify simplify process model model unfoldcomplexity fold configurable simplify, generalize behavior log PAGE 24
  • 26. ProM6 / Uma > www.processmining.org PAGE 25
  • 27. ProM6 / Uma > www.processmining.org PAGE 26
  • 28. ProM6 / Uma > www.processmining.org PAGE 27
  • 29. Experimental Results 15 benchmark logs, 6 industrial logs [www.promtools.org/prom5/] PAGE 28
  • 30. Experimental Results  15 benchmark logs, 6 industrial logs [www.promtools.org/prom5/] model complexity = #arcs / #nodes9.08.07.06.05.04.03.02.01.00.0 PAGE 29
  • 31. Experimental Results precision: traces allowed by model and not in log 1.0 = only log behavior allowed rises/falls within limits (can be controlled) PAGE 30
  • 32. from tospaghetti lasagna?
  • 33. from to less complexspaghetti spaghetti
  • 34. Lessons Learned techniques to navigate the model/behavior space use model and log together use model unfoldings break a rule and see what happens unfold modelcomplexity fold simplify, generalize behavior log PAGE 33
  • 35. And next? process mining process algorithm model readable event simplify process log model process views most simple model covering 80% of the log improve mining algorithms? we showed: there is room for improvement PAGE 34
  • 36. Dirk Fahland about.me/dirk.fahland SimplifyingMined Process Models