INISET@CAiSE 2011

Faculty of Economics and Business Administration Department of Management Information and Operations Management

Jan Claes for INISET@CAiSE 201121 June, 2011

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION

Integrating Computer Log Files for Process Mining

A Genetic Algorithm Inspired Technique

Jan [email protected]://processmining.ugent.beGhent University, Belgium




1. Process Mining


Jan Claes for INISET@CAiSE 20113 / 24

A plane crashed... What happened?

Analyse the ‘black box’



A process failed... What happened?

Analyse the ‘black box’: look for historical data

Process Mining:

Reconstruct and analyse processes

From historical process data

• Log files

• Audit trails

• Database history fields/tables



Process Mining

Processes are supported by IT systems

IT systems record actual process data

Process data can be used to automatically

Discover process model

Check conformance with existing process info

Extend existing process model

Attention

Only As-Is

Only (correctly) recorded information

Process Mining



Preparation

Collect data: find traces

Merge data: from different sources

Structure data: group per instance

Convert data: to tool specific format

Process mining

Make decisions, take action

Process Mining steps



Process Mining steps




2. Merging log files



Example

Product ordering: registered events:

Sales order: document creation (administration)

Delivery: truck load confirmation (warehouse)

Invoice: document creation (administration)

Logging

from administration software

from warehouse software

How to merge both log files?



Example 1

Administration Warehouse

Merge based on matching trace identifiers

SO > Inv

SO > Inv

SO > Inv

SO1

SO2

SO3

Deliver

Deliver

Deliver

SO1

SO2

SO3

SO > Deliver > Inv

SO > Deliver > Inv

SO > Deliver > Inv

SO1

SO2

SO3



Example 2


Merge based on matching attribute values

SO > Inv

SO > Inv

SO > Inv

SO1

SO2

SO3

DeliverDel1

Del2

Del3

SO > Deliver > Inv

SO > Deliver > Inv

SO > Deliver > Inv

SO1

SO2

SO3

(SO1)

Deliver (SO2)

Deliver (SO3)



t1<t2<t3

<<t4<t5<t6

<<t7<t8<t9

Example 3


Merge based on time information

SO > Inv

SO > Inv

SO > Inv

SO1

SO2

SO3

DeliverArr1

Arr2

Arr3

SO > Deliver > Inv

SO > Deliver > Inv

SO > Deliver > Inv

SO1

SO2

SO3

Deliver

Deliver

t1 t3

t4t6

t7 t9

t2

t5

t8



Merging computer log files

Merge based on

Example 1: matching trace identifiers indicator 1

Example 2: matching attribute values indicator 2

Example 3: time information indicator 3

General solution algorithm combining different indicators

Genetic algorithm indicators build up fitness function




3. Genetic algorithm



Genetic algorithm

1st generation 2nd generation 3th generation

cross-over

mutation

survival of the fittest



Genetic algorithm

1st generation 2nd generation 3th generation

mutation

cross-over

survival of the fittest

14

27

6

18

29

5

18

28

32

Fitness function score



Genetic algorithm inspired technique

Find links between traces of both log files and merge them chronologically in new log file

Steps

Make initial solution (best individual links)

Make pseudo-random changes (try to improve score for one specific factor)

Evaluate (keep original or changed solution)

Stop condition (fixed amount of steps)

Only one solution, no cross-over




4. Experiment results



Experiment: proof of concept

Simulated data

Given model

Generate

• random set of logs

• single log (=solution)

Use merge algorithm to merge set of logs

Check resulting log with solution log



Experiment: proof of concept

Advantages of using simulated data

Solution is known

Controllable parameters (e.g. noise, overlap, matching id)

Disadvantages of using simulated data

Limited internal validity (are results realistic?)

No external validity (results not generalisable)



Experiment results

Incorrect links related to total links identified




5. Discussion



Future work

Optimise genetic algorithm

Less incorrect links

Faster implementation (AIS algorithm)

Fitness function factors

Validation with real test cases

Ghent University DPO (Human Resources)

Century21 (Real Estate) & FlexPack (Packaging)

BNP Paribas Fortis (Finance)

...



Contact information

Jan [email protected]

http://processmining.ugent.beTwitter: @janclaesbelgium

INISET@CAiSE 2011

Business

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