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The Automated Discovery 
of Hybrid Processes 
Fabrizio M. Maggi 
University of Tartu 
Tijs Slaats* 
IT University of 
Copenhagen 
Exformatics 
Hajo A. Reijers 
VU University of 
Amsterdam
Overview 
• Hybrid Process Models 
• Discovering Hybrid Process Models 
• Evaluation 
• Future Work + Conclusion
Imperative Process Models
Imperative Process Models 
• Flow-oriented 
• Well-suited to rigid processes 
• In a model with no flow nothing can happen 
• Adding flow allows for additional possible 
behaviors 
• Common in academia and industry
Declarative Process Models
Declarative Process Models 
• Constraint-oriented 
• Well-suited to flexible processes 
• In an unconstrained model anything can 
happen 
• Adding constraints limits behavior 
• Still a novelty in industry
Hybrid Process Models
Hybrid Process Models 
• Different parts of the same process 
may be more or less flexible. 
• Modeling a flexible process imperatively, 
or a strict process declaratively, often 
leads to incomprehensible models. 
• Mixing of paradigms on the sub-process level: 
– Pockets of flexibility in workflow services [Sadiq et al.] 
– Flexibility as a Service (FAAS) [Aalst et al.]
Process Discovery 
Event 
Log 
Process Model
Process Discovery 
• Current discovery techniques: 
– Mining Petri-nets / Flowcharts 
• Alpha miner, Heuristic Miner, ILP miner, … 
– Mining Declarative constraints 
• Declare miner 
• But what if the log contains both flexible and rigid 
parts? 
– Imperative miners tend to blow-up on flexible logs 
– Declarative miners will need to find many constraints to 
model the strict parts of the process and will often have 
trouble finding all of them (resulting in processes with low 
precision) 
• Solution: Hybrid Process Discovery!
Hybrid Process Discovery 
Context 
analysis 
Clustering 
(based on 
context analysis) 
Clustering 
(association rule 
mining) 
Standard 
Process 
Discovery 
Declare 
Discovery 
String Edit 
Distance
Evaluation – BPI Challenge 2012 
Results of Imperative Miners
Evaluation – BPI Challenge 2012 
Result of Hybrid Miner
Evaluation – BPI Challenge 2012 
Comparison of Results 
Fitness 
Size
Future Work 
• Proper plugin for Prom. 
• Visualization of resulting hybrid model. 
• Further evaluation on real cases. 
• Further refinement of the heuristics used in 
the approach, for example the thresholds 
used for determining if an event is structured 
or unstructured.
Conclusion 
• We offer the first automated approach for 
discovering hybrid process models. 
• Using the approach on existing logs gives 
encouraging results: in particular for semi-structured 
logs the discovered models 
become more readable. 
• Plenty of room for future work in an exciting 
new angle on process mining.

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BPM 2014 - The Automated Discovery of Hybrid Processes

  • 1. The Automated Discovery of Hybrid Processes Fabrizio M. Maggi University of Tartu Tijs Slaats* IT University of Copenhagen Exformatics Hajo A. Reijers VU University of Amsterdam
  • 2. Overview • Hybrid Process Models • Discovering Hybrid Process Models • Evaluation • Future Work + Conclusion
  • 4. Imperative Process Models • Flow-oriented • Well-suited to rigid processes • In a model with no flow nothing can happen • Adding flow allows for additional possible behaviors • Common in academia and industry
  • 6. Declarative Process Models • Constraint-oriented • Well-suited to flexible processes • In an unconstrained model anything can happen • Adding constraints limits behavior • Still a novelty in industry
  • 8. Hybrid Process Models • Different parts of the same process may be more or less flexible. • Modeling a flexible process imperatively, or a strict process declaratively, often leads to incomprehensible models. • Mixing of paradigms on the sub-process level: – Pockets of flexibility in workflow services [Sadiq et al.] – Flexibility as a Service (FAAS) [Aalst et al.]
  • 9. Process Discovery Event Log Process Model
  • 10. Process Discovery • Current discovery techniques: – Mining Petri-nets / Flowcharts • Alpha miner, Heuristic Miner, ILP miner, … – Mining Declarative constraints • Declare miner • But what if the log contains both flexible and rigid parts? – Imperative miners tend to blow-up on flexible logs – Declarative miners will need to find many constraints to model the strict parts of the process and will often have trouble finding all of them (resulting in processes with low precision) • Solution: Hybrid Process Discovery!
  • 11. Hybrid Process Discovery Context analysis Clustering (based on context analysis) Clustering (association rule mining) Standard Process Discovery Declare Discovery String Edit Distance
  • 12. Evaluation – BPI Challenge 2012 Results of Imperative Miners
  • 13. Evaluation – BPI Challenge 2012 Result of Hybrid Miner
  • 14. Evaluation – BPI Challenge 2012 Comparison of Results Fitness Size
  • 15. Future Work • Proper plugin for Prom. • Visualization of resulting hybrid model. • Further evaluation on real cases. • Further refinement of the heuristics used in the approach, for example the thresholds used for determining if an event is structured or unstructured.
  • 16. Conclusion • We offer the first automated approach for discovering hybrid process models. • Using the approach on existing logs gives encouraging results: in particular for semi-structured logs the discovered models become more readable. • Plenty of room for future work in an exciting new angle on process mining.