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CRICOS No. 000213Ja university for the worldreal
R
Automated Discovery of
Structured Process Models:
Discover Structured
vs
Discover and Structure
Adriano Augusto, Raffaele Conforti, Marlon Dumas,
Marcello La Rosa, and Giorgio Bruno
CRICOS No. 000213Ja university for the worldreal
R
Automated Process Discovery
CID Task Time Stamp …
13219 Enter Loan Application 2007-11-09 T 11:20:10 -
13219 Retrieve Applicant Data 2007-11-09 T 11:22:15 -
13220 Enter Loan Application 2007-11-09 T 11:22:40 -
13219 Compute Installments 2007-11-09 T 11:22:45 -
13219 Notify Eligibility 2007-11-09 T 11:23:00 -
13219 Approve Simple Application 2007-11-09 T 11:24:30 -
13220 Compute Installements 2007-11-09 T 11:24:35 -
… … … …
Enter Loan
Application
Retrieve
Applicant
Data
Compute
Installments
Approve
Simple
Application
Approve
Complex
Application
Notify
Rejection
Notify
Eligibility
CRICOS No. 000213Ja university for the worldreal
R
Process Quality Dimensions
Process
Discovery
CRICOS No. 000213Ja university for the worldreal
R
Process Quality Dimensions
Process
Discovery
Fitness
CRICOS No. 000213Ja university for the worldreal
R
Process Quality Dimensions
Process
Discovery
Fitness
Precision
CRICOS No. 000213Ja university for the worldreal
R
Process Quality Dimensions
Process
Discovery
Fitness
Precision
Generalization
CRICOS No. 000213Ja university for the worldreal
R
Process Quality Dimensions
Process
Discovery
Fitness
Precision
Generalization
Complexity
CRICOS No. 000213Ja university for the worldreal
R
Process Discovery Algorithms:
The Two Worlds
High-Fitness
High-Precision
High-Fitness
Low-Complexity
CRICOS No. 000213Ja university for the worldreal
R
Process Discovery Algorithms:
The Two Worlds
High-Fitness
High-Precision
Heuristic
Miner
Fodina Miner
High-Fitness
Low-Complexity
CRICOS No. 000213Ja university for the worldreal
R
Process Model discovered with
Heuristics Miner
CRICOS No. 000213Ja university for the worldreal
R
Process Discovery Algorithms:
The Two Worlds
High-Fitness
High-Precision
Heuristic
Miner
Fodina Miner
High-Fitness
Low-Complexity
CRICOS No. 000213Ja university for the worldreal
R
Process Discovery Algorithms:
The Two Worlds
High-Fitness
High-Precision
Heuristic
Miner
Fodina Miner
High-Fitness
Low-Complexity
Inductive
Miner
Evolutionary
Tree Miner
CRICOS No. 000213Ja university for the worldreal
R
Process Model discovered with
Inductive Miner
• Structured by construction
• Based on process tree
CRICOS No. 000213Ja university for the worldreal
R
Process Discovery Algorithms
High-Fitness
High-Precision
Low-Complexity
CRICOS No. 000213Ja university for the worldreal
R
Process Discovery Algorithms
High-Fitness
High-Precision
Low-Complexity
Structured
Miner
CRICOS No. 000213Ja university for the worldreal
R
Process Model discovered with
Structured Miner
CRICOS No. 000213Ja university for the worldreal
R
Discover and Structure:
A two phases approach
• Phase One: discover a process model focussing
on fitness and precision without constraints on
its structure. For example using Heuristic Miner
or Fodina Miner.
• Phase Two: simplify the discovered process
model structuring it at posteriori.
CRICOS No. 000213Ja university for the worldreal
R
Phase Two: Structuring
Discover the RPST of the model
Process Fragment:
• Trivial (T) – single edge
• Polygon (P) – sequence of fragments
• Bond (B) – set of fragments sharing two nodes
• Rigid (R) – none of the above cases
CRICOS No. 000213Ja university for the worldreal
R
Phase Two: Structuring
Discover the RPST of the model
Reject
Payment
Request
Inform
Customer
Payby
Cash
Payby
Cheque
Approve
Update
Account
P1
P1
B1
B1
P3
R1P2
P2 P3
R1
CRICOS No. 000213Ja university for the worldreal
R
Phase Two: Structuring
Discover the RPST of the model
Structure sound AND-Homogeneous
or Heterogeneous rigids using
BPSTruct (Polyvyanyy 2014)
CRICOS No. 000213Ja university for the worldreal
R
Phase Two: Structuring
Discover the RPST of the model
Structure sound AND-Homogeneous
or Heterogeneous rigids using
BPSTruct (Polyvyanyy 2014)
Structure XOR-Homogeneous and
unsound rigids using Extended
Oulsnam
CRICOS No. 000213Ja university for the worldreal
R
Oulsnam’s Algorithm Extended for
BPMN Process Models
• Injection
CRICOS No. 000213Ja university for the worldreal
R
Oulsnam’s Algorithm Extended for
BPMN Process Models
• Push-Down
– Push down-stream the gateway causing the injection
– Duplicate everything in between the gateway causing
the injection and the gateway down-stream
CRICOS No. 000213Ja university for the worldreal
R
Oulsnam’s Algorithm Extended for
BPMN Process Models
• Ejection
CRICOS No. 000213Ja university for the worldreal
R
Oulsnam’s Algorithm Extended for
BPMN Process Models
• Pull-Up
– Pull up-stream the gateway causing the injection
– Duplicate everything in between the gateway causing
the injection and the gateway up-stream
CRICOS No. 000213Ja university for the worldreal
R
Evaluation Setup
• Real-Life dataset: IBM (54 models) and SAP
(545 models) collections
• Synthetic dataset: 20 models
• Generated three sets of logs for a total of 619
logs
• We retained all logs for which Heuristics Miner
produced an unstructured model - 129 logs
CRICOS No. 000213Ja university for the worldreal
R
Evaluation Setup
• Four process discovery algorithms:
– Inductive Miner
– Evolutionary Tree Miner
– Heuristics Miner
– Structured Miner (on top of Heuristics Miner)
• Four quality dimensions:
– Fitness
– Precision
– Generalization
– Complexity
CRICOS No. 000213Ja university for the worldreal
R
Evaluation Results
• Real-life datasets:
CRICOS No. 000213Ja university for the worldreal
R
Evaluation Results
• Real-life datasets:
CRICOS No. 000213Ja university for the worldreal
R
Heuristics Miner - Real-life Dataset
CRICOS No. 000213Ja university for the worldreal
R
Inductive Miner - Real-life Dataset
CRICOS No. 000213Ja university for the worldreal
R
Structured Miner - Real-life Dataset
CRICOS No. 000213Ja university for the worldreal
R
Future Work
• Experiment with alternative discovery algorithms to
explore alternative tradeoffs between model quality
metrics
• Explore the option of sacrificing weak bisimilarity to
obtain models with higher structuredness
• Use process model clone detection techniques to
refactor duplicates introduced by the structuring phase
CRICOS No. 000213Ja university for the worldreal
R
Questions
?

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Automated Discovery of Structured Process Models: Discover Structured vs Discover and Structure

  • 1. CRICOS No. 000213Ja university for the worldreal R Automated Discovery of Structured Process Models: Discover Structured vs Discover and Structure Adriano Augusto, Raffaele Conforti, Marlon Dumas, Marcello La Rosa, and Giorgio Bruno
  • 2. CRICOS No. 000213Ja university for the worldreal R Automated Process Discovery CID Task Time Stamp … 13219 Enter Loan Application 2007-11-09 T 11:20:10 - 13219 Retrieve Applicant Data 2007-11-09 T 11:22:15 - 13220 Enter Loan Application 2007-11-09 T 11:22:40 - 13219 Compute Installments 2007-11-09 T 11:22:45 - 13219 Notify Eligibility 2007-11-09 T 11:23:00 - 13219 Approve Simple Application 2007-11-09 T 11:24:30 - 13220 Compute Installements 2007-11-09 T 11:24:35 - … … … … Enter Loan Application Retrieve Applicant Data Compute Installments Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility
  • 3. CRICOS No. 000213Ja university for the worldreal R Process Quality Dimensions Process Discovery
  • 4. CRICOS No. 000213Ja university for the worldreal R Process Quality Dimensions Process Discovery Fitness
  • 5. CRICOS No. 000213Ja university for the worldreal R Process Quality Dimensions Process Discovery Fitness Precision
  • 6. CRICOS No. 000213Ja university for the worldreal R Process Quality Dimensions Process Discovery Fitness Precision Generalization
  • 7. CRICOS No. 000213Ja university for the worldreal R Process Quality Dimensions Process Discovery Fitness Precision Generalization Complexity
  • 8. CRICOS No. 000213Ja university for the worldreal R Process Discovery Algorithms: The Two Worlds High-Fitness High-Precision High-Fitness Low-Complexity
  • 9. CRICOS No. 000213Ja university for the worldreal R Process Discovery Algorithms: The Two Worlds High-Fitness High-Precision Heuristic Miner Fodina Miner High-Fitness Low-Complexity
  • 10. CRICOS No. 000213Ja university for the worldreal R Process Model discovered with Heuristics Miner
  • 11. CRICOS No. 000213Ja university for the worldreal R Process Discovery Algorithms: The Two Worlds High-Fitness High-Precision Heuristic Miner Fodina Miner High-Fitness Low-Complexity
  • 12. CRICOS No. 000213Ja university for the worldreal R Process Discovery Algorithms: The Two Worlds High-Fitness High-Precision Heuristic Miner Fodina Miner High-Fitness Low-Complexity Inductive Miner Evolutionary Tree Miner
  • 13. CRICOS No. 000213Ja university for the worldreal R Process Model discovered with Inductive Miner • Structured by construction • Based on process tree
  • 14. CRICOS No. 000213Ja university for the worldreal R Process Discovery Algorithms High-Fitness High-Precision Low-Complexity
  • 15. CRICOS No. 000213Ja university for the worldreal R Process Discovery Algorithms High-Fitness High-Precision Low-Complexity Structured Miner
  • 16. CRICOS No. 000213Ja university for the worldreal R Process Model discovered with Structured Miner
  • 17. CRICOS No. 000213Ja university for the worldreal R Discover and Structure: A two phases approach • Phase One: discover a process model focussing on fitness and precision without constraints on its structure. For example using Heuristic Miner or Fodina Miner. • Phase Two: simplify the discovered process model structuring it at posteriori.
  • 18. CRICOS No. 000213Ja university for the worldreal R Phase Two: Structuring Discover the RPST of the model Process Fragment: • Trivial (T) – single edge • Polygon (P) – sequence of fragments • Bond (B) – set of fragments sharing two nodes • Rigid (R) – none of the above cases
  • 19. CRICOS No. 000213Ja university for the worldreal R Phase Two: Structuring Discover the RPST of the model Reject Payment Request Inform Customer Payby Cash Payby Cheque Approve Update Account P1 P1 B1 B1 P3 R1P2 P2 P3 R1
  • 20. CRICOS No. 000213Ja university for the worldreal R Phase Two: Structuring Discover the RPST of the model Structure sound AND-Homogeneous or Heterogeneous rigids using BPSTruct (Polyvyanyy 2014)
  • 21. CRICOS No. 000213Ja university for the worldreal R Phase Two: Structuring Discover the RPST of the model Structure sound AND-Homogeneous or Heterogeneous rigids using BPSTruct (Polyvyanyy 2014) Structure XOR-Homogeneous and unsound rigids using Extended Oulsnam
  • 22. CRICOS No. 000213Ja university for the worldreal R Oulsnam’s Algorithm Extended for BPMN Process Models • Injection
  • 23. CRICOS No. 000213Ja university for the worldreal R Oulsnam’s Algorithm Extended for BPMN Process Models • Push-Down – Push down-stream the gateway causing the injection – Duplicate everything in between the gateway causing the injection and the gateway down-stream
  • 24. CRICOS No. 000213Ja university for the worldreal R Oulsnam’s Algorithm Extended for BPMN Process Models • Ejection
  • 25. CRICOS No. 000213Ja university for the worldreal R Oulsnam’s Algorithm Extended for BPMN Process Models • Pull-Up – Pull up-stream the gateway causing the injection – Duplicate everything in between the gateway causing the injection and the gateway up-stream
  • 26. CRICOS No. 000213Ja university for the worldreal R Evaluation Setup • Real-Life dataset: IBM (54 models) and SAP (545 models) collections • Synthetic dataset: 20 models • Generated three sets of logs for a total of 619 logs • We retained all logs for which Heuristics Miner produced an unstructured model - 129 logs
  • 27. CRICOS No. 000213Ja university for the worldreal R Evaluation Setup • Four process discovery algorithms: – Inductive Miner – Evolutionary Tree Miner – Heuristics Miner – Structured Miner (on top of Heuristics Miner) • Four quality dimensions: – Fitness – Precision – Generalization – Complexity
  • 28. CRICOS No. 000213Ja university for the worldreal R Evaluation Results • Real-life datasets:
  • 29. CRICOS No. 000213Ja university for the worldreal R Evaluation Results • Real-life datasets:
  • 30. CRICOS No. 000213Ja university for the worldreal R Heuristics Miner - Real-life Dataset
  • 31. CRICOS No. 000213Ja university for the worldreal R Inductive Miner - Real-life Dataset
  • 32. CRICOS No. 000213Ja university for the worldreal R Structured Miner - Real-life Dataset
  • 33. CRICOS No. 000213Ja university for the worldreal R Future Work • Experiment with alternative discovery algorithms to explore alternative tradeoffs between model quality metrics • Explore the option of sacrificing weak bisimilarity to obtain models with higher structuredness • Use process model clone detection techniques to refactor duplicates introduced by the structuring phase
  • 34. CRICOS No. 000213Ja university for the worldreal R Questions ?