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Beyond Tasks and Gateways: 
Discovering BPMN Models 
with subprocesses, boundary events 
and activity markers 
Raffaele Conforti, Marcello La Rosa 
Queensland University of Technology 
Marlon Dumas, Luciano García-Bañuelos 
University of Tartu 
BPM’2014 Conference, Eindhoven 11 September 2014 1
2 
Automated Process Discovery 
Enter Loan 
Application 
Retrieve 
Applicant 
Data 
Compute 
Installments 
Approve 
Simple 
Application 
Approve 
Complex 
Application 
Notify 
Rejection 
Notify 
Eligibility 
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 - 
… … … …
What’s the catch?
There you are!
Automated Process Discovery: 
Handling Complexity 
Filter 
• Filter out “irrelevant” events (tasks) 
• Filter out “irrelevant” traces 
Abstract 
• Zoom into most frequent tasks or paths 
• Extract subprocesses 
Divide 
• Divide log by variants based on similarity (trace clustering) 
• Discover multiple process models rather than one 
5
Related Work: ProM two-phase miner 
Bose, Veerbeck & van det Aalst: Discovering Hierarchical Process Models using ProM
ProM Two-Phase Miner 
7 
Produces this… 
Instead of…
Before 
8
After 
9
What’s the catch?
Data! 
11
Extracting the Process Hierarchy 
Extract 
event 
tables 
ReceivePO 
Find 
primary 
keys 
Find 
foreign 
keys 
Cluster 
event 
types 
Split log 
per 
cluster 
12 
flat log 
log 
hierarchy 
Time POID Att2 Att3 
11-24 17:12 1 … … 
11-25 08:53 2 … … 
… … … … 
CreateMO 
Time MOID POID Att3 
11-24 17:13 1 1 … 
11-26 09:31 2 2 … 
… 2 … … 
ShipPO 
Time POID Att2 
11-25 
12:11 
1 … 
… 2 … 
… … …
… the rest 
Discover one 
model per (sub- 
)process 
Identify 
interrupting 
boundary events 
Identify 
interrupting timer 
events 
Identify event 
subprocesses 
Identify 
loop/multi-instance 
markers 
13 
- Heuristics miner 
- ILP 
- Inductive Miner 
- Fodina Heuristics
Evaluation Setup 
Logs Traces Events Event types Events/type 
IWT (FRIS) 121 1472 13 113 
Insurance 896 12437 9 1382 
Order-to-cash 3000 32896 13 2530 
• Four flat process discovery algos 
• Heuristics, ILP, Inductive (H), Fodina 
• Each algo with and without BPMN-Miner 
• Quality measures 
• Accuracy: Fitness, precision, F-score 
• Understandability: Size, Control-Flow Complexity, … 
14
Evaluation - Results 
15
Side-Effect: Correct Models 
16
What’s Next 
• Standalone tool implementation 
• Currently in ProM nightly build 
• Further evaluation 
• Logs with larger number of event types 
• Noise resilience 
• Missing events can trick foreign key discovery 
• Further enrichment 
• Event-based gateways, more BPMN events… 
• Adding data conditions, completion conditions, … 
17

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Beyond Tasks and Gateways: Automated Discovery of BPMN Models with Subprocesses, Boundary Events and Activity Markers

  • 1. Beyond Tasks and Gateways: Discovering BPMN Models with subprocesses, boundary events and activity markers Raffaele Conforti, Marcello La Rosa Queensland University of Technology Marlon Dumas, Luciano García-Bañuelos University of Tartu BPM’2014 Conference, Eindhoven 11 September 2014 1
  • 2. 2 Automated Process Discovery Enter Loan Application Retrieve Applicant Data Compute Installments Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility 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 - … … … …
  • 5. Automated Process Discovery: Handling Complexity Filter • Filter out “irrelevant” events (tasks) • Filter out “irrelevant” traces Abstract • Zoom into most frequent tasks or paths • Extract subprocesses Divide • Divide log by variants based on similarity (trace clustering) • Discover multiple process models rather than one 5
  • 6. Related Work: ProM two-phase miner Bose, Veerbeck & van det Aalst: Discovering Hierarchical Process Models using ProM
  • 7. ProM Two-Phase Miner 7 Produces this… Instead of…
  • 12. Extracting the Process Hierarchy Extract event tables ReceivePO Find primary keys Find foreign keys Cluster event types Split log per cluster 12 flat log log hierarchy Time POID Att2 Att3 11-24 17:12 1 … … 11-25 08:53 2 … … … … … … CreateMO Time MOID POID Att3 11-24 17:13 1 1 … 11-26 09:31 2 2 … … 2 … … ShipPO Time POID Att2 11-25 12:11 1 … … 2 … … … …
  • 13. … the rest Discover one model per (sub- )process Identify interrupting boundary events Identify interrupting timer events Identify event subprocesses Identify loop/multi-instance markers 13 - Heuristics miner - ILP - Inductive Miner - Fodina Heuristics
  • 14. Evaluation Setup Logs Traces Events Event types Events/type IWT (FRIS) 121 1472 13 113 Insurance 896 12437 9 1382 Order-to-cash 3000 32896 13 2530 • Four flat process discovery algos • Heuristics, ILP, Inductive (H), Fodina • Each algo with and without BPMN-Miner • Quality measures • Accuracy: Fitness, precision, F-score • Understandability: Size, Control-Flow Complexity, … 14
  • 17. What’s Next • Standalone tool implementation • Currently in ProM nightly build • Further evaluation • Logs with larger number of event types • Noise resilience • Missing events can trick foreign key discovery • Further enrichment • Event-based gateways, more BPMN events… • Adding data conditions, completion conditions, … 17