Automated Discovery of Structured Process Models: Discover Structured vs Discover and Structure
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Transcript of Automated Discovery of Structured Process Models: Discover Structured vs Discover and Structure
![Page 1: Automated Discovery of Structured Process Models: Discover Structured vs Discover and Structure](https://reader030.fdocuments.net/reader030/viewer/2022020213/5875d3ea1a28ab8f438b5c3b/html5/thumbnails/1.jpg)
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
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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 -
… … … …
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CRICOS No. 000213Ja university for the worldreal R
Process Quality Dimensions
Process DiscoveryProcess
Discovery
Fitness
Precision
Generalization
Complexity
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CRICOS No. 000213Ja university for the worldreal R
Process Quality Dimensions
Process Discovery
Fitness
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CRICOS No. 000213Ja university for the worldreal R
Process Quality Dimensions
Process Discovery
Fitness
Precision
![Page 6: Automated Discovery of Structured Process Models: Discover Structured vs Discover and Structure](https://reader030.fdocuments.net/reader030/viewer/2022020213/5875d3ea1a28ab8f438b5c3b/html5/thumbnails/6.jpg)
CRICOS No. 000213Ja university for the worldreal R
Process Quality Dimensions
Process Discovery
Fitness
Precision
Generalization
![Page 7: Automated Discovery of Structured Process Models: Discover Structured vs Discover and Structure](https://reader030.fdocuments.net/reader030/viewer/2022020213/5875d3ea1a28ab8f438b5c3b/html5/thumbnails/7.jpg)
CRICOS No. 000213Ja university for the worldreal R
Process Quality Dimensions
Process Discovery
Fitness
Precision
Generalization
Complexity
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CRICOS No. 000213Ja university for the worldreal R
Process Discovery Algorithms:The Two Worlds
High-FitnessHigh-Precision
High-FitnessLow-Complexity
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CRICOS No. 000213Ja university for the worldreal R
Process Discovery Algorithms:The Two Worlds
High-FitnessHigh-Precision
Heuristic Miner
Fodina Miner
High-FitnessLow-Complexity
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CRICOS No. 000213Ja university for the worldreal R
Process Model discovered with Heuristics Miner
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CRICOS No. 000213Ja university for the worldreal R
Process Discovery Algorithms:The Two Worlds
High-FitnessHigh-Precision
Heuristic Miner
Fodina Miner
High-FitnessLow-Complexity
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CRICOS No. 000213Ja university for the worldreal R
Process Discovery Algorithms:The Two Worlds
High-FitnessHigh-Precision
Heuristic Miner
Fodina Miner
High-FitnessLow-Complexity
Inductive Miner
Evolutionary Tree Miner
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CRICOS No. 000213Ja university for the worldreal R
Process Model discovered with Inductive Miner
• Structured by construction• Based on process tree
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CRICOS No. 000213Ja university for the worldreal R
Process Discovery Algorithms
High-FitnessHigh-Precision
Low-Complexity
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CRICOS No. 000213Ja university for the worldreal R
Process Discovery Algorithms
High-FitnessHigh-Precision
Low-ComplexityStructured
Miner
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CRICOS No. 000213Ja university for the worldreal R
Process Model discovered with Structured Miner
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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.
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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
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CRICOS No. 000213Ja university for the worldreal R
Phase Two: Structuring
Discover the RPST of the model
P1
P1
B1
B1
P3
R1P2
P2 P3
R1
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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)
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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
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CRICOS No. 000213Ja university for the worldreal R
Oulsnam’s Algorithm Extended for BPMN Process Models
• Injection
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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
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CRICOS No. 000213Ja university for the worldreal R
Oulsnam’s Algorithm Extended for BPMN Process Models
• Ejection
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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
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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
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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
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CRICOS No. 000213Ja university for the worldreal R
Evaluation Results
• Real-life datasets:
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CRICOS No. 000213Ja university for the worldreal R
Evaluation Results
• Real-life datasets:
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CRICOS No. 000213Ja university for the worldreal R
Heuristics Miner - Real-life Dataset
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CRICOS No. 000213Ja university for the worldreal R
Inductive Miner - Real-life Dataset
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CRICOS No. 000213Ja university for the worldreal R
Structured Miner - Real-life Dataset
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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
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CRICOS No. 000213Ja university for the worldreal R
Questions
?