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

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Genetic Process MiningGenetic Process Mining

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

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 noise, duplicate activities, non-free choice and invisible tasks

Internal Representation

Fitness Measure

Genetic Operators


GPM – Internal Representation

• Causal Matrix

Input

XX AA BB CC DD EE YY Output

XX

AA

BB

CC

DD

EE

YY



• 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



• 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



• Causal Matrix– Compact representation

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

TaskTask InputInput OutputOutput

XX {}{} {{A,B}}{{A,B}}

AA {{X}}{{X}} {{C},{D}}{{C},{D}}

BB {{X}}{{X}} {{C},{E}}{{C},{E}}

CC {{A,B}}{{A,B}} {{D,E}}{{D,E}}

DD {{A},{C}}{{A},{C}} {{Y}}{{Y}}

EE {{B},{C}}{{B},{C}} {{Y}}{{Y}}

YY {{D},{E}}{{D},{E}} {}{}



• Causal Matrix– Semantics


AA {}{} {{B},{C,D}}{{B},{C,D}}

BB {{A}}{{A}} {{E,F}}{{E,F}}

CC {{A}}{{A}} {{E}}{{E}}

DD {{A}}{{A}} {{F}}{{F}}

EE {{B},{C}}{{B},{C}} {{G}}{{G}}

FF {{B},{D}}{{B},{D}} {{G}}{{G}}

GG {{E},{F}}{{E},{F}} {}{}

Invisible tasks only fire to enable visible tasks!



• Causal Matrix– Semantics


AA {}{} {{B},{C,D}}{{B},{C,D}}

BB {{A}}{{A}} {{E,F}}{{E,F}}

CC {{A}}{{A}} {{E}}{{E}}

DD {{A}}{{A}} {{F}}{{F}}

EE {{B},{C}}{{B},{C}} {{G}}{{G}}

FF {{B},{D}}{{B},{D}} {{G}}{{G}}

GG {{E},{F}}{{E},{F}} {}{}

Deadlock!

Invisible tasks only fire to enable visible tasks!



• Causal Matrix– Mappings


AA {}{} {{B},{C,D}}{{B},{C,D}}

BB {{A}}{{A}} {{E,F}}{{E,F}}

CC {{A}}{{A}} {{E}}{{E}}

DD {{A}}{{A}} {{F}}{{F}}

EE {{B},{C}}{{B},{C}} {{G}}{{G}}

FF {{B},{D}}{{B},{D}} {{G}}{{G}}

GG {{E},{F}}{{E},{F}} {}{}



• Causal Matrix– Mappings


AA {}{} {{C,D}}{{C,D}}

BB {}{} {{D}}{{D}}

CC {{A}}{{A}} {}{}

DD {{A,B}}{{A,B}} {}{}


GPM – Fitness Measure

• 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:

SS,A,B,C,D,EE

A

B

Original net

E

C

DSS EE

A

B

I ndividual

E

C

DSS EE

For noise-free, fitness punishes:

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


Trace:

SS,A,B,C,D,EE

For noise-free, fitness punishes:

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

A

B

Original net

E

C

DSS EE

A

B

I ndividual

E

C

DSS EE


Fitness - How to assess an individual’s fitness?


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

Fitness = 1


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

reachable marking


Fitness Measure

where

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


Genetic Operators

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

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


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– Apply the genetic process mining to "real-life" logs


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

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

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