The document discusses repairing process models to improve conformance to event logs. It presents an approach for repairing models that involves aligning the log and model, identifying sublogs of events that cannot be replayed, and using these to add optional/remove activities or add subprocesses to the model. The approach was implemented in ProM and evaluated on a case study, showing it can effectively repair models while maintaining a low distance to the original model.

1. Dirk Fahland
Wil van der Aalst
Repairing Process Models

2. Situation: model vs. reality
process
model
Conformance Check
Is this a good model
for the real process?
event log
PAGE 1

3. Discovery vs. Repair
original Whyrepaired processdiscovered
not use discovery?
PAGE 2

4. Problem Statement
repaired
process
repair process
model
model
Conformance Check
Is this a good model
for the real process?
event log
PAGE 3

5. Conformance
process
model fitness precision
model can model behavior
replay the log close to the log
generalization simplicity
event log model is as simple
model allows more than
as possible
just the log
PAGE 4

6. Here: focus on fitness
process
model fitness precision
preserve during
repair
post-processing
[Fahland, Aalst, BPM2011]
generalization simplicity
event log pre-process log
(filter noise, etc.)
PAGE 5

7. Problem Analysis
repaired
process
repair process
model
model
fitness
replays all traces
repaired discovered
replays some traces original model
distance to
original model
PAGE 6

8. Approach to Model Repair
repaired
process remove process
model non-required model
conformance deviations
checker
add
missing
event log
PAGE 7

9. Approach to Model Repair
repaired
process remove process
model non-required model
conformance deviations
checker
add
missing
event log
PAGE 8

10. Align Log and Model
p2 p4
p1 B
model: A p3 p5 E
p6
C
D
ABFBDCE
log
PAGE 9

11. Align Log and Model
p2 p4
p1 B
model: A p3 p5 E
p6
C
D
ABFBDCE
log alignment
PAGE 10

12. Align: Synchronous Move on A
p2 p4
p1 B
model: A p3 p5 E
p6
C
D
A
ABFBDCE A
p2
p3
log alignment
PAGE 11

13. Align: Synchronous Move on B
p2 p4
p1 B
model: A p3 p5 E
p6
C
D
A B
ABFBDCE A B
p2 p4
p3 p3
log alignment
PAGE 12

14. Align: Log Move on F
p2 p4
p1 B
model: A p3 p5 E
p6
C
D
A B
ABFBDCE A B F
p2 p4
p3 p3
log alignment
PAGE 13

15. Align: Log Move on B
p2 p4
p1 B
model: A p3 p5 E
p6
C
D
A B
ABFBDCE A B F B
p2 p4
p3 p3
log alignment
PAGE 14

16. Align: Model Move on C
p2 p4
p1 B
model: A p3 p5 E
p6
C
D
A B C
ABFBDCE A B F B
p2 p4 p4
p3 p3 p5
log alignment
PAGE 15

17. Align: Synchronous Move on D
p2 p4
p1 B
model: A p3 p5 E
p6
C
D
A B C D
ABFBDCE A B F B D
p2 p4 p4 p4
p3 p3 p5 p3
log alignment
PAGE 16

18. Align: Synchronous Move on C
p2 p4
p1 B
model: A p3 p5 E
p6
C
D
A B C D C
ABFBDCE A B F B D C
p2 p4 p4 p4 p4
p3 p3 p5 p3 p5
log alignment
PAGE 17

19. Align: Synchronous Move on E
p2 p4
p1 B
model: A p3 p5 E
p6
C
D
A B C D C E
ABFBDCE A B F B D E
p2 p4 p4 p4 p4 p6
p3 p3 p5 p3 p5
log alignment
PAGE 18

20. Complete Alignment
p2 p4
p1 B
model: A p3 p5 E
p6
C
D
A B C D C E firing sequence
ABFBDCE A B F B D C E trace
p2 p4 p4 p4 p4 p6 visited markings
p3 p3 p5 p3 p5
of the model
log alignment
PAGE 19

21. Diagnostic Information
FB sublog @ {p4,p3}
need to replay FB
when in marking {p4,p3} need to skip C to replay trace
A B C D C E firing sequence
A B F B D C E trace
p2 p4 p4 p4 p4 p6 visited markings
p3 p3 p5 p3 p5
of the model
PAGE 20

22. Approach to Model Repair
repaired
process make optional / process
model remove model
activities that have to
be skipped/removed
conformance add
checker subprocesses
discover
discover
event log
sublogs of events
that cannot be replayed
PAGE 21

23. Sublogs: Join by Events and Location
p2 p4
p1 B
A p3 p5 E
p6
C
D
GH @ {p2,p3} FB @ {p4,p3}
conformance
checker
GH @ {p4,p3} BF @ {p4,p5}
sublogs @ locations PAGE 22

24. Sublogs: Join by Events and Location
p2 p4
p1 B
A p3 p5 E
p6
C
D
GH FB
@ {p3} @ {p4}
GH BF
sublogs @ locations PAGE 23

25. Sublog of Events  Add Subprocess
B
p2 p4 F
p1 B
A p3 p5 E
p6
C
D
GH FB
@ {p3} @ {p4}
GH BF
PAGE 24

26. Sublog of Events  Add Subprocess
B
p2 p4 F
p1 B
A p3 p5 E
p6
C
D
H
G
GH FB
@ {p3} @ {p4}
GH BF
PAGE 25

27. Events to Skip
B
p2 p4 F
p1 B
A p3 p5 E
p6
C
D
H
G
allow to skip C
conformance
checker
PAGE 26

28. Events to Skip
B
p2 p4 F
p1 B
A p3 p5 E
p6
C
D
H
G
allow to skip C
conformance
checker
PAGE 27

29. Approach to Model Repair
repaired
process make optional / process
model remove model
activities that have to
be skipped/removed
conformance add
checker subprocesses
discover
discover
event log
sublogs of events
that cannot be replayed
PAGE 28

30. Implemented: ProM > Uma > Repair Model
municipality process + log
model moves: 3327
log moves: 310
deviations per trace: 2-49
PAGE 29

31. Implemented: ProM > Uma > Repair Model
municipality process + log
model moves: 3327
log moves: 310
deviations per trace: 2-49
PAGE 30

32. Implemented: ProM > Uma > Repair Model
municipality process + log
model moves: 3327
log moves: 310
deviations per trace: 2-49
PAGE 31

33. Implemented: ProM > Uma > Repair Model
municipality process + log (filtered)
model moves: 681
log moves: 229
deviations per trace: 1-12
PAGE 32

34. Implemented: ProM > Uma > Repair Model
municipality process + log (filtered)
model moves: 681
log moves: 229
deviations per trace: 1-12
PAGE 33

35. Implemented: ProM > Uma > Repair Model
PAGE 34

36. Implemented: ProM > Uma > Repair Model
PAGE 35

37. Case Study: Deviations vs. Similarity
distance to original [Dijkman et al., BPM 2009]
 1 reference model 1
 10 logs
.8
• 180-481 traces,
up to 82 events per trace
.6
• 2-49 deviations per trace
rediscovered
.4 + simplified
 results
• distance to original < .2
.2
• better than rediscovery repaired
• stable in # of deviations
0 2 4 6 8 10 12
avg. deviations per trace
PAGE 36

38. Conclusion: new tool in the box
PAGE 37

39. Conclusion
 effective technique for model repair
• alignment  sublogs of missing events  subprocess
 future work:
• quality of alignment  quality of repair
• allow to change ordering of tasks
• repair for precision, generalization, …
• many more …
PAGE 38

40. Dirk Fahland
Wil van der Aalst
Repairing Process Models

41. Conformance
4 quality measures
process
model fitness precision
“flower
model can model” behavior
replay the log not explained
by log is small
generalization simplicity
event log model is as simple
model allows more than
as possible
just the log
PAGE 40

42. Conformance
4 quality measures
process
model fitness precision
“each trace
model can separately” behavior
replay the log not explained
by log is small
generalization simplicity
event log model is as simple
model allows more than
as possible
just the log
PAGE 41

43. Conformance
4 quality measures
process
model fitness “spaghetti” precision
model can behavior
replay the log not explained
by log is small
generalization simplicity
event log model is as simple
model allows more than
as possible
just the log
PAGE 42