The document discusses a metaheuristic optimization framework for automated business process discovery, focusing on the effective extraction of process models from event logs through Directly-Follows Graph (DFG) approaches. It emphasizes optimizing model quality using various algorithmic strategies, comparing fitness and precision, and iterating through configurations to select the best-performing models. Future work includes expanding the framework with additional DFG-based approaches and exploring alternative quality measures.

1. Metaheuristic Optimization
for Automated Business Process Discovery
Adriano Augusto, Marlon Dumas, Marcello La Rosa

2. Context
Automated discovery of (business) process models from event logs
Automated
Process Discovery
Approach (APDA)
2
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3. Process Model Quality
How good is an automatically discovered process model?
Process
Model
APDA
3
Event
Log
Compare
Fitness, Precision (F-score)
Generalization
Simplicity
Soundness

4. State-of-the-art
4
Automated Process
Discovery Approaches
APDAs based on
Directly-Follows Graphs
(DFG-based APDAs)
Split Miner
Inductive Miner
Fodina Miner
Heuristics Miner
…

5. DFG-based APDAs
5
Process
Model
DFG-based
APDA
(e.g. Split Miner)
Event
Log
Discover
DFG
Manipulate
DFG
(e.g. Filtering)
Convert DFG
to Model
(e.g. BPMN)
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6. DFG-based APDAs
6
Process
Model
DFG-based
APDA
(e.g. Split Miner)
Event
Log
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input params
6
Discover
DFG
Manipulate
DFG
(e.g. Filtering)
Convert DFG
to Model
(e.g. BPMN)

7. What is the best input configuration?
Model
(1)
DFG-based
APDA
(e.g. Split Miner)
Log
Configuration 1
Assess
Quality
Model
Quality (1)
Model
(2)
DFG-based
APDA
(e.g. Split Miner)
Log
Configuration 2
Assess
Quality
Model
Quality (2)
Model
(N)
DFG-based
APDA
(e.g. Split Miner)
Log
Configuration N
Assess
Quality
Model
Quality (N)
Compare

8. What is the best input configuration?
Model
(1)
DFG-based
APDA
(e.g. Split Miner)
Log
Configuration 1
Assess
Quality
Model
Quality (1)
Model
(2)
DFG-based
APDA
(e.g. Split Miner)
Log
Configuration 2
Assess
Quality
Model
Quality (2)
Model
(N)
DFG-based
APDA
(e.g. Split Miner)
Log
Configuration N
Assess
Quality
Model
Quality (N)
Compare
Model (x)
is the
BEST!

9. How to be more efficient?
9
Optimization Metaheuristics
Population Based
Evolutionary computation
Ant colony
Bee colony
Swarm particles
…
Single-solution Based
Repetitive local search
Iterative local search
Tabu search
Simulated annealing
…

10. Adapting the Metaheuristics to our Context
10
Repetitive Local Search (RLS)
Iterative Local Search (ILS)
Tabu Search (TS)
Simulated Annealing (SA)
1. Solution Space
2. Solution Neighbourhood
3. Objective Function

11. Adapting the Metaheuristics to our Context
Model
(1)
DFG-based
APDA
(e.g. Split Miner)
Log
Configuration 1
Assess
Quality
Model
Quality (1)
Model
(2)
DFG-based
APDA
(e.g. Split Miner)
Log
Configuration 2
Assess
Quality
Model
Quality (2)
Model
(N)
DFG-based
APDA
(e.g. Split Miner)
Log
Configuration N
Assess
Quality
Model
Quality (N)
Compare
Model (x)
is the
BEST!
Solution Space Objective Function
Neighbours?

12. Optimizing a DFG-based APDAs
12
Event
Log
Discover
DFG
Manipulate
DFG
(e.g. Filtering)
Convert DFG
to Model
(e.g. BPMN)
input params

13. Optimizing a DFG-based APDAs
13
Event
Log
Discover
DFG
Manipulate
DFG
(e.g. Filtering)
Convert DFG
to Model
(e.g. BPMN)
input params
Assess
Quality

14. Assess Quality
 Fitness, precision, generalization, or simplicity?
 What measure to use?
Assess
Quality
Fitness and precision > F-score
Alignment, anti-alignment, PCC, entropy, Markovian accuracy

15. Optimizing a DFG-based APDAs
15
Event
Log
Discover
DFG
Manipulate
DFG
(e.g. Filtering)
Convert DFG
to Model
(e.g. BPMN)
input params
Assess
Quality

16. Optimizing a DFG-based APDAs
16
Event
Log
Discover
DFG
Manipulate
DFG
(e.g. Filtering)
Convert DFG
to Model
(e.g. BPMN)
input params
Explore
Neighbour
DFGs
Assess
Quality

17. Explore Neighbour DFGs
Explore
Neighbour
DFGs
 Given a DFG, its closer neighbours are the ones having one more or one less edge.
 Adding edges will result into adding behaviour (increasing the fitness of the model)
 Removing edges will result into removing behaviour (increasing the precision of the model)
Explore
Neighbour
DFGs
DFG DFG
DFG
DFG
DFG
DFGModel
Quality

18. Optimizing a DFG-based APDAs
18
Event
Log
Discover
DFG
Manipulate
DFG
(e.g. Filtering)
Convert DFG
to Model
(e.g. BPMN)
input params
Explore
Neighbour
DFGs
Assess
Quality
Convert
DFGs to
Models

19. Optimizing a DFG-based APDAs
19
Event
Log
Discover
DFG
Manipulate
DFG
(e.g. Filtering)
Convert DFG
to Model
(e.g. BPMN)
input params
Explore
Neighbour
DFGs
Assess
Quality
Convert
DFGs to
Models
Assess
Quality

20. Optimizing a DFG-based APDAs
20
Event
Log
Discover
DFG
Manipulate
DFG
(e.g. Filtering)
Convert DFG
to Model
(e.g. BPMN)
input params
Explore
Neighbour
DFGs
Assess
Quality
Select Best
DFG
Candidate
Convert
DFGs to
Models
Assess
Quality

21. Optimizing a DFG-based APDAs
21
Event
Log
Discover
DFG
Manipulate
DFG
(e.g. Filtering)
Convert DFG
to Model
(e.g. BPMN)
input params
Explore
Neighbour
DFGs
Check
Termination
Condition
Assess
Quality
Select Best
DFG
Candidate
Convert
DFGs to
Models
Assess
Quality
Timeout
Number of iterations
Objective function threshold

22. Optimizing a DFG-based APDAs
22
Process
Model
Event
Log
Discover
DFG
Manipulate
DFG
(e.g. Filtering)
Convert DFG
to Model
(e.g. BPMN)
input params
Explore
Neighbour
DFGs
Check
Termination
Condition fulfilled
not
fulfilled
Assess
Quality
Select Best
DFG
Candidate
Convert
DFGs to
Models
Assess
Quality
Optimization Metaheuristic

23. Optimization Framework
23
APDA – Metaheuristic Interface
Event
Log
Input
Settings
Objective
FunctionsOptimization metaheuristics ID
APDA ID
Objective Function ID
Process
Model
Optimization
Metaheuristics
DFG-based
APDAs

24. Optimization Framework Instantiation
24
APDA – Metaheuristic Interface
Event
Log
Input
Settings
Markovian
F-scoreOptimization metaheuristics ID
APDA ID
Objective Function ID
Process
Model
RLS, ILS,
TS, SA
Split Miner

25. Evaluation Setup
25
— 20 real-life event logs (10 BPIC logs, RTFMP, SEPSIS case, and 8 private logs)
— 3 baselines without hyper-parameters optimization:
Inductive Miner (IM), Evolutionary Tree Miner (ETM), Split Miner (SM)
— 1 baseline with hyper-parameters optimization, Split Miner (HPO)
— Markovian accuracy, Alignment accuracy, simplicity, and time performance

26. MarkvovianF-score
Event Logs

27. Limitations
27
— Slower than baselines with default input params (Inductive Miner, Split Miner)
— More complex models when optimizing fitness
— Not applicable to any APDA, only for DFG-based APDA

28. Thanks for attending!
Questions?
28

29. Future Work
29
— Add more DFG-based APDAs to our framework
(Fodina Miner and Inductive Miner)
— Explore alternative quality measures to drive the optimization metaheuristics
— Combine accuracy and simplicity measures

30. Results – Markvovian F-score 30

31. Results – Alignment F-score 31

32. Optimizing a DFG-based APDAs
32
Process
Model
Event
Log
Discover
DFG
Manipulate
DFG
(e.g. Filtering)
Convert DFG
to Model
(e.g. BPMN)
input params
Explore
Neighbour
DFGs
Check
Termination
Condition fulfilled
not
fulfilled
Assess
Quality
Select Best
DFG
Candidate
Convert
DFGs to
Models
Assess
Quality
Optimization Metaheuristic