Alignment based Precision Checking

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This slide tells you how to measure precision between process model and its recorded execution. Presented at BPI 2012, Tallinn, Estonia, by Arya Adriansyah. The technique is implemented in ProM 6.x, package ETConformance (see http://processmining.org).

Transcript of Alignment based Precision Checking

Alignment-based

Precision CheckingA. Adriansyah1, J. Munoz Gamma2,

J. Carmona2, B.F. van Dongen1,

W.M.P. van der Aalst1

Tallinn, 3 September 2012

1) Department of Mathematics and Computer Science,

Eindhoven University of Technology, The Netherlands

2) Software Department, Universitat Politècnica de

Catalunya, Spain

Model needs to be precise

1

Event Log

Process Models

a d

a b

b

cend

c

a b

d

end

aabd

abd

acd

aabd

c

a d

2

Event Log

Process Modelsc

a b

d

end

aabd

abd

acd

aabd

c

ad

Unfitting

d

a d

a b

b

cend

a

b

c

b

a

d

14 4

2

d

1

1

1d

2 2

0

0

b

c

0

d0

d

c0…

0a

0c

Prefix automaton

Reference: J. Muñoz-Gama, J. Carmona (2010). A

Fresh Look at Precision in Process Conformance.

8th International Conference of Business Process

Management (BPM): 211-226.

How to measure precision for unfitting

event logs?

Overview

3

aabbd

adabd

a d

Unfitting Log

Process Model

Alignment Automaton

Experiments

aabd

aabd

acd

Fitting TracesAlignment

Precision

Metrics

Optimal alignment: least #deviations

4

a d

a b

b

cend

Trace adab

aAlignment

d b

a a b

a

d

Process Model

aabd Reference: W.M.P. van der Aalst, A.

Adriansyah, B.F. van Dongen (2012).

Replaying history on process models

for conformance checking and

performance analysis. WIREs Data

Mining and Knowledge

Discovery, 2(2), 182-192.

Optimal alignments can be many

5

a d

a b

b

cend

Trace ad

Alignment 1 Alignment 2

Process Model

ba

a d

d ca

a d

d

Both alignments are optimal

abd or acd

Overview

6

aabbd

adabd

a d

Unfitting Log

Process Model

Alignment Automaton

Experiments Precision

Metrics

aabd

aabd

acd

Fitting TracesAlignment

a22 3

db1.51.5 11

b1a

d

3

All-Alignment Automaton (Prefix-based)

7

c

Fitting Traces

a d

a b

b

cend

Process Model

All-Alignments Automaton

aabd

abd

abd or acd

aabd

1 1

0.5d

0.5

4 4

2 2 2

a

b

b

a

d

24 4

2 2 2

d2

aabd

abd

abd or acd

aabd

1-Alignment automaton (Prefix-based)

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Fitting Traces

a d

a b

b

cend

Process Model

1-Align Automaton

aabd

abd

abd

aabd

b d

b

a

ad

4 4

2 2 2

1-Alignment automaton (Prefix-based)

9

0c

Fitting Traces

a d

a b

b

cend

Process Model

1-Align Automaton

aabd

abd

abd

aabd

Imprecision

2 2

1-Alignment

Comparison of log automata

10

aabd

abd

ad

adab

Event Log

a da b

bc

end

Process Model

Without alignment

All-Alignments

Pre

fix-b

ased

Lo

g A

uto

mata

aabd

abd

abd/acd

aabd

Fitting Traces

Overview

11

aabbd

adabd

a d

Unfitting Log

Process Model

Alignment Automaton

Experiments

aabd

aabd

acd

Fitting TracesAlignment

Precision

Metrics

Reference: J. Muñoz-Gama, J. Carmona (2010). A Fresh Look at Precision in

Process Conformance. 8th International Conference of Business Process

Management (BPM): 211-226.

Overview

12

aabbd

adabd

a d

Unfitting Log

Process Model

Alignment Automaton

Experiments

aabd

aabd

acd

Fitting Traces

Precision

Metrics

Alignment

Implementation in ProM 6

• Package: “ETConformance”

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Experiment settings

14

Event Log

Flower (F)

Precise (P)

Flower’ (F’)

Precise’ (P’)

Duplicate Log Combined Log

Precise-Precise (PP’)

Precise-Flower

(PF’)

Flower -Precise

(FP’)

Flower-Flower

(FF’)

Experiment 1: Measuring precision stability

15

Perfectly fitting logs and models Non-fitting logs and models

P FP’ PF’ PP’P FP’ PF’ PP’

Experiment 2: Sensitivity to unfitting trace

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Model P

0.80

0.85

0.90

0.95

1.00

0 1 2 3 4

0.00

0.20

0.40

0.60

0.80

1.00

0 1 2 3 4

Pre

cisi

on

Number of Removed Events

Noise Sensitivity - P

ETC

1-Align

All-Align

Pre

cis

ion

#Removed events

0.00

0.20

0.40

0.60

0.80

1.00

0 1 2 3 4

Model PF’

Pre

cis

ion

#Removed events

0.00

0.10

0.20

0.30

0.40

0 1 2 3 4

Pre

cis

ion

#Removed events

Model FP’

0.90

0.93

0.95

0.98

1.00

0 1 2 3 4

Pre

cis

ion

#Removed events

Model PP’

Experiment 3: Real life cases

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Overview

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aabbd

adabd

a d

Unfitting Log

Process Model

Alignment Automaton

Experiments

aabd

aabd

acd

Fitting Traces

Precision

Metrics

Alignment

Conclusion and Future Work

19

Questions

20

Thanks!

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