faculteit technologie management genetic process mining ana karla medeiros ton weijters wil van der...

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Genetic Process MiningGenetic Process Mining

Ana Karla Medeiros Ana Karla Medeiros Ton Weijters Ton Weijters Wil van der Aalst Wil van der Aalst

Eindhoven University of Technology

Department of Information Systems

[email protected]


Outline

• Process Mining

• Genetic Algorithms

• Genetic Process Mining – Internal Representation– Fitness measure– Genetic Operators

• Experiments and Results

• Conclusion and Future Work


Process Mining

X = apply for licenseA = classes motobikeB = classes carC = theoretical exam

C = theoretical examD = practical motorbike examE = practical car examY = get result


Process Mining (cont.)

• Most of the current techniques cannot handle– Structural constructs: non-free choice, duplicate tasks

and invisible tasks– Noisy logs– Reason: local approach


Outline

• Process Mining


• Genetic Process Mining– Internal Representation– Fitness measure– Genetic Operators




Genetic Algorithms

– Global approach local optimum

global optimum


Outline

• Process Mining


• Genetic Process Mining– Internal Representation– Fitness measure– Genetic Operators




Genetic Process Mining (GPM)

Aim: Use genetic algorithm to tackle non-free choice, invisible tasks, duplicate tasks and noise.

Internal Representation

Fitness Measure

Genetic Operators


GPM – Build the Initial Population

• Causal Matrix

Input

XX AA BB CC DD EE YY Output

XX

AA

BB

CC

DD

EE

YY

XA

BY

EC

D


XA

BY

EC

D


• Causal Matrix

Input


XX 0 1 1 0 0 0 0

AA 0 0 0 1 1 0 0

BB 0 0 0 1 0 1 0

CC 0 0 0 0 1 1 0

DD 0 0 0 0 0 0 1EE 0 0 0 0 0 0 1YY 0 0 0 0 0 0 0



• Causal Matrix

Input


XX 0 1 1 0 0 0 0 A \/ B

AA 0 0 0 1 1 0 0 C /\ D

BB 0 0 0 1 0 1 0 C /\ E

CC 0 0 0 0 1 1 0 D \/ E

DD 0 0 0 0 0 0 1 Y

EE 0 0 0 0 0 0 1 Y

YY 0 0 0 0 0 0 0 True

XA

BY

EC

D



• Causal Matrix

Input True X X A \/ B A /\ C B /\ C D \/ E


XX 0 1 1 0 0 0 0 A \/ B

AA 0 0 0 1 1 0 0 C /\ D

BB 0 0 0 1 0 1 0 C /\ E

CC 0 0 0 0 1 1 0 D \/ E

DD 0 0 0 0 0 0 1 Y

EE 0 0 0 0 0 0 1 Y

YY 0 0 0 0 0 0 0 True

XA

BY

EC

D



• Every individual has the same amount of tasks

(1) Log

X

A

B Y

C

ED

(2) Set of tasks

(3) Randomly created individuals

X

A

BY

C

E

D

X

AB

Y

C ED


GPM – Calculate Fitness

• Main idea– Benefit the individuals that can parse more frequent

material in the log

• Challenges– How to assess an individual’s fitness?– How to punish individuals that allow for undesired extra

behavior?


Fitness - How to assess an individual’s fitness?

- Use continuous semantics parser and register problems L = log and CM = causal matrix


Trace:

X,A,C,D,Y

For noise-free, fitness punishes:

AND-join AND-join OR-join OR-join OR-split OR-split AND-split AND-split

XA

BY

EC

D

Original net

XA

BY

EC

D

Individual


Trace:

X,A,C,D,Y

For noise-free, fitness punishes:

AND-split AND-split OR-split OR-split OR-join OR-join AND-join AND-join

XA

BY

EC

D

Original net

Individual

XA

BY

EC

D


Fitness - How to assess an individual’s fitness?


Fitness - How to punish individuals that allow for undesired extra behavior?

Fitness = 1

XA

BY

EC

D

X

AB

Y

C ED

X

A

B

YC

E

D


XA

BY

EC

D

X

AB

Y

C ED

X

A

B

YC

E

D

Fitness - How to punish individuals that allow for undesired extra behavior?- Count the amount of enabled tasks at every

reachable marking


GPM – Calculate Fitness

where

L = log and CM = causal matrix and CM[] = population


GPM – Create next population

• Genetic operators– Crossover

• Recombines existing material in the population• Crossover point = task• Crossover probability• Subsets are swapped

– Mutation• Introduce new material in the population• Every task of a individual can be mutated• Mutation probability


GPM – Create next population

• Genetic operators - Crossover

XA

BY

EC

DX

A

BY

EC

DParent 1 Parent 2

XA

BY

EC

DX

A

BY

EC

D

Offspring 1 Offspring 2


Outline

• Process Mining






Experiments and Results

• Experiments– ProM framework

• Genetic Algorithm Plug-in• http://www.processmining.org

– Simulated data

• Results– The genetic algorihm found models that could parse all

the traces in the log


ProM framework – Genetic Algorithm Plug-in


Outline

• Process Mining






Conclusion and Future Work

• Conclusion– Genetic algorithms can be used to mine process

models

• Future Work– Tackle duplicate tasks

• How to detect the right level of abstraction?

– Apply the genetic process mining to "real-life" logs• How to deal with noise?


http://www.processmining.orghttp://www.processmining.org

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