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 -

… … … …

CRICOS No. 000213Ja university for the worldreal R

Process Quality Dimensions

Process DiscoveryProcess

Discovery

Fitness

Precision

Generalization

Complexity

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-FitnessHigh-Precision

High-FitnessLow-Complexity

CRICOS No. 000213Ja university for the worldreal R

Process Discovery Algorithms:The Two Worlds

High-FitnessHigh-Precision

Heuristic Miner

Fodina Miner

High-FitnessLow-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-FitnessHigh-Precision

Heuristic Miner

Fodina Miner

High-FitnessLow-Complexity

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

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-FitnessHigh-Precision

Low-Complexity

CRICOS No. 000213Ja university for the worldreal R

Process Discovery Algorithms

High-FitnessHigh-Precision

Low-ComplexityStructured

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

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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