Research paper presentation at the 35th International Conference on Conceptual Modeling (ER'2016), Gifu, Japan, 15 Nov. 2016 Presentation delivered by Raffaele Conforti. Paper available at: http://goo.gl/5EN3l2

1. CRICOS No. 000213Ja university for the worldreal
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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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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

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Process Quality Dimensions
Process
Discovery

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Process Quality Dimensions
Process
Discovery
Fitness

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Process Quality Dimensions
Process
Discovery
Fitness
Precision

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Process Quality Dimensions
Process
Discovery
Fitness
Precision
Generalization

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R
Process Quality Dimensions
Process
Discovery
Fitness
Precision
Generalization
Complexity

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Process Discovery Algorithms:
The Two Worlds
High-Fitness
High-Precision
High-Fitness
Low-Complexity

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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
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Process Model discovered with
Heuristics Miner

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Process Discovery Algorithms:
The Two Worlds
High-Fitness
High-Precision
Heuristic
Miner
Fodina Miner
High-Fitness
Low-Complexity

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Process Discovery Algorithms:
The Two Worlds
High-Fitness
High-Precision
Heuristic
Miner
Fodina Miner
High-Fitness
Low-Complexity
Inductive
Miner
Evolutionary
Tree Miner

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Process Model discovered with
Inductive Miner
• Structured by construction
• Based on process tree

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Process Discovery Algorithms
High-Fitness
High-Precision
Low-Complexity

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R
Process Discovery Algorithms
High-Fitness
High-Precision
Low-Complexity
Structured
Miner

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Process Model discovered with
Structured Miner

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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
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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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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

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Phase Two: Structuring
Discover the RPST of the model
Structure sound AND-Homogeneous
or Heterogeneous rigids using
BPSTruct (Polyvyanyy 2014)

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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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Oulsnam’s Algorithm Extended for
BPMN Process Models
• Injection

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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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Oulsnam’s Algorithm Extended for
BPMN Process Models
• Ejection

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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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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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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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Evaluation Results
• Real-life datasets:

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Evaluation Results
• Real-life datasets:

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Heuristics Miner - Real-life Dataset

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Inductive Miner - Real-life Dataset

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Structured Miner - Real-life Dataset

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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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Questions
?