INISET@CAiSE 2011

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION Integrating Computer Log Files for Process Mining A Genetic Algorithm Inspired Technique Jan Claes jan.claes@ugent.be http://processmining.ugent.be Ghent University, BelgiumFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 21 June, 2011

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION 1. Process MiningFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 21 June, 2011

A plane crashed... What happened?Analyse the ‘black box’Faculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 3 / 24

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/tablesFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 4 / 24

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 Process Mining  Only As-Is  Only (correctly) recorded informationFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 5 / 24

Process Mining steps 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 actionFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 6 / 24

Process Mining stepsFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 7 / 24

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION 2. Merging log filesFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 21 June, 2011

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?Faculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 9 / 24

Example 1Administration Warehouse SO1 SO > Inv SO1 Deliver SO2 SO > Inv SO2 Deliver SO3 SO > Inv SO3 Deliver SO1 SO > Deliver > Inv SO2 SO > Deliver > Inv SO3 SO > Deliver > Inv Merge based on matching trace identifiersFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 10 / 24

Example 2Administration Warehouse SO1 SO > Inv Del1 Deliver (SO1) SO2 SO > Inv Del2 Deliver (SO2) SO3 SO > Inv Del3 Deliver (SO3) SO1 SO > Deliver > Inv SO2 SO > Deliver > Inv SO3 SO > Deliver > Inv Merge based on matching attribute valuesFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 11 / 24

Example 3Administration t1<t2<t3 Warehouse << SO1 SO t > Inv t Arr1 Deliver t 1 3 t4<t5<t6 2 SO2 SO t > Inv t 6 << Arr2 Deliver t 4 5 SO3 SO t > Inv t t7<t8<t9 Arr3 Deliver t 7 9 8 SO1 SO > Deliver > Inv SO2 SO > Deliver > Inv SO3 SO > Deliver > Inv Merge based on time informationFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 12 / 24

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 functionFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 13 / 24

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION 3. Genetic algorithmFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 21 June, 2011

Genetic algorithm cross-over survival of the fittest mutation 1st generation 2nd generation 3th generationFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 15 / 24

Genetic algorithm Fitness function score 14 18 18 cross-over 27 29 28 survival of the fittest mutation 6 5 32 1st generation 2nd generation 3th generationFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 16 / 24

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-overFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 17 / 24

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION 4. Experiment resultsFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 21 June, 2011

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 logFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 19 / 24

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)Faculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 20 / 24

Experiment resultsIncorrect links related to total links identifiedFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 21 / 24

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION 5. DiscussionFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 21 June, 2011

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)  ...Faculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 23 / 24

Contact information Jan Claes jan.claes@ugent.be http://processmining.ugent.be Twitter: @janclaesbelgiumFaculty of Economics and Business Administration Jan Claes for INISET@CAiSE 2011Department of Management Information and Operations Management 24 / 24

http://processmining.ugent.be	85
http://www.janclaes.info	57
http://janclaes.info	11

INISET@CAiSE 2011

by Jan Claes

Accessibility

Categories

Upload Details

Usage Rights

3 Embeds 153

Statistics

INISET@CAiSE 2011 Presentation Transcript