The document discusses process mining as a set of methods for analyzing business processes based on event logs, highlighting operations such as model discovery, conformance checking, and model repair. It presents two automatic approaches to process model repair: trace-alignment based and impact-driven. Additionally, the document emphasizes the importance of interactive and incremental model repair, allowing users to effectively reconcile differences between models and event logs.

1. Interactive and Incremental
Process Model Repair
Abel Armas Cervantes, Nick R.T.P. van Beest, Marcello La Rosa,
Marlon Dumas and Luciano García-Bañuelos
1

2. Process mining
Process mining is a family of methods for analyzing business processes based
on event logs.
2

3. Process mining
Process mining is a family of methods for analyzing business processes based
on event logs.
• Some of the most important process mining operations:
• Discovery
Model
Log
3

4. Process mining
Process mining is a family of methods for analyzing business processes based
on event logs.
• Some of the most important process mining operations:
• Discovery
• Conformance checking
Model v1
Log
4

5. Process mining
Process mining is a family of methods for analyzing business processes based
on event logs.
• Some of the most important process mining operations:
• Discovery
• Conformance checking
• Enhancement
• Repair
Model v1
Log
Model v2
5

6. Process model repair at a glance
Model v1
Log
Conformance
Checking
Diagnosis Repair
Model v2
Behavioral-based
(true concurrency
semantics)
Textual descriptions and
graphical
representations
Interactive and
incremental
6

7. Process
Quality
Dimensions
Fitness
Simplicity
Precision
Generalization
Does the model follow
the Occam’s razor
principle?
Does the model
contain additional
undesired behavior?
Does the model
contain additional
welcomed behavior?
Is the model able
to replay the event
log?
Log
⟨A,B⟩
⟨A,B⟩
⟨A,B⟩
Log
⟨A⟩
⟨A,A⟩
⟨A,A,A⟩
7
Process quality dimensions

8. Process quality dimensions
8
Process
Quality
Dimensions
Fitness
Simplicity
Precision
Generalization
Fitness

9. Approach 1: Model Repair - Aligning Process Models to
Reality
• Automatic
• Trace-alignment based (interleaving semantics)
• Sub-logs of non-fitting traces are extracted from the log and repaired by
inserting cycles or sub-processes
• Fitness based
• Extend the model such that it explains the observed behavior
D. Fahland, and W. M.P. van der Aalst, Model Repair - Aligning Process Models to Reality. IS 2015 9

10. Approach 2 - Impact-Driven Process Model
Repair
• Automatic
• Trace-alignment based (interleaving semantics)
• Fitness based subject to a budget
• Maximize fitness
• Define cost to repairs
• Apply most impactful model repairs
• Insert self-loops for moves on log
• Skip labels for moves on model
A. Polyvyanyy, W. M. P. van der Aalst, A. H. M. ter Hofstede, and M. T. Wynn: Impact-Driven Process Model Repair. ACM TOSEM 2016 10

11. Automatic process model repair in action
11
modelFigure1
A B C D E
F
G
A B C D E
F
G
F
E H
A B C D E
F
G H
A B C D E
F
G
Approach 1
modelFigure1
A B C D E
F
G
A B C D E
F
G
F
E H
A B C D E
F
G H
Approach 2
modelFigure1
A B C D E
F
G
A B C D E
F
G
F
E H
A B C D E
F
G H
A B C D E
F
G
Log
⟨A,B,C,D,E,F,G,H⟩
⟨A,B,C,D,F,E,G,H⟩

12. Automatic process model repair in action
I
B
A B C
A B C D OI
X
D
X
O
I A B X
A
X O
A
C D
modelFigure2
I
B
A B C
X
D
X
O
I A B X
A
X
A
C D
modelFigure2
I
B
A B C
A B C D OI
X
D
X
O
I A B X
A
X O
A
C D
Approach 1 Approach 2
Log
⟨I, A, B, X, C, O⟩
⟨I, A, B, X, D, O⟩
⟨I, B, A, X, C, O⟩
⟨I, B, A, X, D, O⟩

13. Log or model as absolute truth
• Log can contain noise or negative deviances from expected behavior
Don’t you fully trust the model? Don’t you fully trust the log?
Select what to repair in the modelRepair the model
13
Model v1
Model v2
Model v1
Model v2

14. Interactive and incremental repair
• Conformance checker based on behavioral-alignment
• Support users during the reconciliation of differences
• Let the users decide what, when and how to repair
14
modelFigure1
A B C D E
F
G
A B C D E
F
G
F
E H
A B C D E G H
Log
⟨A,B,C,D,E,F,G,H⟩
⟨A,B,C,D,F,E,G,H⟩
model
A DB C E
F
G
H
modelFigure1
A B C D E
F
G
A B C D E
F
G
F
E H
A B C D E
F
G H

15. How do we do that?
• Adopt true concurrency semantics:
• Event structures (ES) as a unified representation for logs and models
• Identify common and deviant behavior
• Describe differences via natural language statements
L. García-Bañuelos, N. R.T.P. van Beest , M. Dumas, and M. La Rosa, and W. Mertens: Complete and interpretable conformance checking of
business processes. TSE 2017 15
Difference
statements
Event log
Input model
PESM
unfold
PESL
merge
Partially
Synchronized
Product (PSP)
compare
extract
differences

16. From an event log to a PES
Log Trace Ref N
A B C E t1 3
A C B E t2 2
A B E t3 2
A D E t4 3
Runs
{e0,f0,g0}:A
{e1,f1}:B
{f2}:E {e3}:E {g2}:E
{e2}:C {g1}:D
e0:A
e1:B e2:C
e3:E
f0:A
f1:B
f2:E
g0:A
g1:D
g2:E
t1, t2 → p1 t3 → p2 t4 → p3
PES
16

17. From model to PES
BPMN model
Petri net
17
Complete prefix unfolding

18. From model to PES
Complete prefix unfolding
PES
18

19. Behavioral alignment overview
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
Trace Ref N
A B C E t1 3
A C B E t2 2
A B E t3 2
A D E t4 3
A
B
D
E
C
f0:A
f1:B f2:C f3:D
f4:E f5:E
19

20. match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
Behavioral alignment overview
20

21. 21
Behavioral alignment overview
match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E

22. 22
match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
Behavioral alignment overview

23. 23
match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
Behavioral alignment overview

24. 24
match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
Behavioral alignment overview

25. 25
match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
Behavioral alignment overview

26. 26
match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
Behavioral alignment overview

27. In the log, C is optional
after {A,B}, whereas in the
model it is not
27
match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
Log PES Model PES
e0:A
e1:B e2:C e3:D
e4:E e5:E e6:E
f0:A
f1:B f2:C f3:D
f4:E f5:E
Behavioral alignment overview

28. Patterns for behavioral alignment
diagnosis
Unfitting behavior:
• Relation mismatch:
1. Causality-Concurrency
2. Conflict
• Event mismatch:
3. Task skipping
4. Task substitution
5. Unmatched repetition
6. Task relocation
7. Task insertion / absence
28
Additional model behavior:
8. Unobserved acyclic interval
9. Unobserved cyclic interval

29. Repair assistance through patterns
match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
In the log, C is optional after
{A,B}, whereas in the model
it is not
2
A
B
C
E
D
ExampleCoopis
A
B
C
E
D
29

30. Repair patterns extension
• Distinct sets of patterns can be used to explain the encountered difference
match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
lh = {}, rh = {f2 : C, fx:E}
m = {(e0,f0)A, (e1,f1)B}
rhide E
• In the log, C is optional after {A,B}, whereas in the model
it is not
• In the log, task E does not occur after {A,B}
• In the log, task C does not occur after {A,B}
• In the log, task E does not occur after {A,B}
• In the log, the interval [C, E] is optional after {A,B},
whereas in the model it is not
30

31. Order of pattern detection
1. Patterns based on intervals
2. Patterns based on binary relations
3. Patterns based on a single task
i a b o
i o
c
ba
i a b o bc c
d
e
i a ob c
i a oec
TaskReloc: In the log, the interval [b,c] occurs after [i,a,o] instead of [i,a]
ConcConf: In the log, after i, Task a and Task b are concurrent, while in the model they
are mutually exclusive
i
a
o
b
i a o
b
i
a
o
b
i
a
o
b
i
a
o
b
TaskIns: In the log, Task b occurs after [i, a] and before o
New Process
i a b o
i a b o
i o
c
ba
i a b o b
i a o b
i a o
b

32. Repair patterns based on intervals
i a b o
i o
c
ba
i a b o bc c
d
e
i a ob c
i a oec
Intervals
i a b o
i a b o
i o
c
ba
i a b o bc c
d
e
i a ob c
c
i a b o bc c
e
i a ob c
i a oec
i o
c
ba
i a b o bc c
d
e
i a ob c
i a oec
32
Intervals
i a b o
i a b o
i o
c
ba
i a b o bc c
d
e
i a ob c
i a b o
i o
c
ba
i a b o bc c
d
e
i a ob c
i a oec
i a o
b c

33. Repair patterns based on binary relations
i a ob
i a ob
i a ob
i a ob
i a ob
i a ob
i a ob
i a ob
i
a
o
b
i a o
b
i
a
o
b
i
a
o
b
i
a
o
b
b
i
a
o
b
i a o
b
i
a
o
b
i
a
o
b
i
a
o
i a o
b
i
a
o
b
i a o
b
i
a
o
b
i
a
o
b
i
a
o
i
a
o
b
b
i
a
o
b
i
a
o
b
i
a
o
b
33

34. Repair patterns based on single tasks
NewProcess2
i a ob
d c e
i a ob
i a ob
i a b o
i a b o
i o
c
ba
i a b o b
i a o b
b
i a o
b
34

35. Impact of repairs
• Most impactful (proportion of traces affected by the change) repairs are
presented first
35
In the log, F occurs after {A,B}
match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
lhide F
(100)
(100)
(90)
(90)
(10)
Impact: 10/100 = 0.1

36. Impact of repairs
• Most impactful (proportion of traces affected by the change) repairs are
presented first
36
In the log, F occurs after {A,B}
match B
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B}
rhide Cmatch C
lh = {}, rh = {f2:C}
m = {(e0,f0)A,(e1,f1)B}
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C}
lh = {}, rh = {}
m = {(e0,f0)A}
match A
lh = {}, rh = {}
m = {}
match E
lh = {}, rh = {}
m = {(e0,f0)A,(e1,f1)B,(e2,f2)C,(e5,f4)E}
lhide F
(100)
(100)
(10)
(10)
(90)
Impact: 90/100 = 0.9

37. Interactive and incremental repair
37
Powered by

38. Future work
• Perform a more extensive evaluation on real-life datasets
• Consider additional information in the log to define a more refined notion of
impact
• Identify sets of discrepancies that can be applied together