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Innovations in Genetic Process Mining - Towards Efficient Process Model Discovery

Explore the use of genetic algorithms for efficient process model discovery in noisy and complex event logs. This study introduces genetic process mining techniques for handling duplicate tasks, invisible activities, and non-free choices, improving upon traditional methods. Experiments demonstrate the effectiveness of genetic algorithms in achieving global optimum process models. Learn about internal representation, fitness measures, genetic operators, and how genetic algorithms can revolutionize process mining. Discover the potential of genetic process mining in real-life applications and the future research directions.

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Innovations in Genetic Process Mining - Towards Efficient Process Model Discovery

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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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