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Genetic Process Mining

Genetic Process Mining. Wil van der Aalst Ana Karla Medeiros Ton Weijters Eindhoven University of Technology D epartment of Information Systems a.k.medeiros@tm.tue.nl. Outline. Process Mining Genetic Algorithms Genetic Process Mining Internal Representation Fitness measure

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Genetic Process Mining

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  1. Genetic Process Mining Wil van der Aalst Ana Karla Medeiros Ton Weijters Eindhoven University of Technology Department of Information Systems a.k.medeiros@tm.tue.nl

  2. Outline • Process Mining • Genetic Algorithms • Genetic Process Mining • Internal Representation • Fitness measure • Genetic Operators • Experiments and Results • Conclusion and Future Work

  3. Outline • Process Mining • Genetic Algorithms • Genetic Process Mining • Internal Representation • Fitness measure • Genetic Operators • Experiments and Results • Conclusion and Future Work

  4. Process Mining X = apply for license A = classes motobike B = classes car C = theoretical exam C = theoretical exam D = practical motorbike exam E = practical car exam Y = get result

  5. Process Mining (cont.) • Most of the current techniques cannot handle • Structural constructs: non-free choice, duplicate tasks and invisible tasks • Noisy logs • Reason: local approach

  6. Outline • Process Mining • Genetic Algorithms • Genetic Process Mining • Internal Representation • Fitness measure • Genetic Operators • Experiments and Results • Conclusion and Future Work

  7. local optimum global optimum Genetic Algorithms • Global approach

  8. Outline • Process Mining • Genetic Algorithms • Genetic Process Mining • Internal Representation • Fitness measure • Genetic Operators • Experiments and Results • Conclusion and Future Work

  9. Genetic Process Mining (GPM) Aim: Use genetic algorithm to tackle noise, duplicate activities, non-free choice and invisible tasks Internal Representation Fitness Measure Genetic Operators

  10. GPM – Internal Representation • Causal Matrix

  11. GPM – Internal Representation • Causal Matrix

  12. GPM – Internal Representation • Causal Matrix

  13. GPM – Internal Representation • Causal Matrix

  14. GPM – Internal Representation • Causal Matrix • Compact representation

  15. GPM – Internal Representation • Causal Matrix • Semantics Invisible tasks only fire to enable visible tasks!

  16. Deadlock! GPM – Internal Representation • Causal Matrix • Semantics Invisible tasks only fire to enable visible tasks!

  17. GPM – Internal Representation • Causal Matrix • Mappings 

  18. GPM – Internal Representation • Causal Matrix • Mappings 

  19. GPM – Fitness Measure • Main idea • Benefit the individuals that can parse more frequent material in the log • Challenges • How to assess an individual’s fitness? • How to punish individuals that allow for undesired extra behavior?

  20. L = log and CM = causal matrix Fitness - How to assess an individual’s fitness? - Use continuous semantics parser and register problems

  21. Trace: SS,A,B,C,D,EE For noise-free, fitness punishes: OR-split  AND-split AND-join  OR-join

  22. Trace: SS,A,B,C,D,EE For noise-free, fitness punishes: OR-join  AND-join AND-split  OR-split

  23. Fitness - How to assess an individual’s fitness?

  24. Fitness - How to punish individuals that allow for undesired extra behavior? Fitness = 1

  25. Fitness - How to punish individuals that allow for undesired extra behavior? • - Count the amount of enabled tasks at every reachable marking

  26. Fitness Measure L = log and CM = causal matrix and CM[] = population • where

  27. Genetic Operators • Crossover • Recombines existing material in the population • Crossover probability • Crossover point = task • Subsets are swapped • Mutation • Introduce new material in the population • Mutation probability • Every task of a individual can be mutated

  28. Outline • Process Mining • Genetic Algorithms • Genetic Process Mining • Internal Representation • Fitness measure • Genetic Operators • Experiments and Results • Conclusion and Future Work

  29. Experiments and Results • Experiments • ProM framework • Genetic Algorithm Plug-in • http://www.processmining.org • Simulated data • Results • The genetic algorihm found models that could parse all the traces in the log

  30. ProM framework – Genetic Algorithm Plug-in

  31. ProM framework – Genetic Algorithm Plug-in

  32. Outline • Process Mining • Genetic Algorithms • Genetic Process Mining • Internal Representation • Fitness measure • Genetic Operators • Experiments and Results • Conclusion and Future Work

  33. Conclusion and Future Work • Conclusion • Genetic algorithms can be used to mine process models • Future Work • Tackle duplicate tasks • Apply the genetic process mining to "real-life" logs

  34. http://www.processmining.org

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