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FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION




Merging Computer Log Files for Process Mining:
   An Artificial Immun...
Process Mining

Processes are supported by IT systems
IT systems record actual process data
Process data can be used to...
Process data in event logs




                                                                                       Even...
Process Mining steps

 Preparation
             Collect data: find event information
             Merge data: from diff...
Merging log files




                              My research:
                             Merging log files



Ghent U...
Merging log files




1. Find links between traces               2. Merge events chronologically   3. Add unlinked traces
...
Find links

Required properties of solution
        Finds traces in both log files that belong to the
         same proc...
Find links

Proposed solution
       Take the best possible guess based on assumptions
       Include multiple indicato...
Decisions to make

Which indicator factors?
How to calculate a score for each factor?
How to combine factor scores to g...
Indicator factors

Same trace identifier
        Assumption: If both logs contain a trace with the
         same id, the...
Indicator factors

Equal attribute values
        Assumption: The more attributes of a trace and its
         events fro...
Test results

Simulated data (300-400 msec on standard laptop)
        Benefit of controllable parameters, known solutio...
New approach

Rule Based Merger
        User has to configure rules for linking traces
        Rule = relationship betw...
New approach




Ghent University, Faculty of Economics and Business Administration   Jan Claes for EIS 2011
Department of...
Contact information




                                                Jan Claes
                                        ...
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Slides of my presentation at EIS conference, 31 October 2011, Delft, NL

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EIS 2011

  1. 1. FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION Merging Computer Log Files for Process Mining: An Artificial Immune System Technique Jan Claes and Geert Poels http://processmining.ugent.be Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 30 October, 2011
  2. 2. Process Mining Processes are supported by IT systems IT systems record actual process data Process data can be used to  Discover process model  Check conformance with existing process info  Improve or extend existing process model Attention Process Mining  Only As-Is  Only (correctly) recorded information Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 2 / 15
  3. 3. Process data in event logs Event log The process Process support Grouped events Recorded events Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 3 / 15
  4. 4. Process Mining steps  Preparation  Collect data: find event information  Merge data: from different sources  Structure data: group per instance  Convert data: to tool specific format  Process mining  Make decisions, take action Manual task Analysts needed in most cases Automated task Less human involvement needed Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 4 / 15
  5. 5. Merging log files My research: Merging log files Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 5 / 15
  6. 6. Merging log files 1. Find links between traces 2. Merge events chronologically 3. Add unlinked traces Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 6 / 15
  7. 7. Find links Required properties of solution  Finds traces in both log files that belong to the same process execution  Without prior knowledge about the provided log files (as generic as possible)  But with maximal possibilities for the (expert) user to include his knowledge about the log files Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 7 / 15
  8. 8. Find links Proposed solution  Take the best possible guess based on assumptions  Include multiple indicator factors in analysis  Calculate factor scores for each analysed solution  Combine factor scores into global score per solution  ‘Best guess’ is solution with highest combined score, because based on assumed indicators, most indicator value points to this solution  Provide user interaction possibilities Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 8 / 15
  9. 9. Decisions to make Which indicator factors? How to calculate a score for each factor? How to combine factor scores to global score? Which solutions to analyse? (analyse = calculate & compare scores) Which user interactions to include (expert) user knowledge? See paper for more details Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 9 / 15
  10. 10. Indicator factors Same trace identifier  Assumption: If both logs contain a trace with the same id, there is a very high chance they match  Not always though (e.g. customer id vs. order id) 16 10 17 12 18 14 19 16 20 18 21 20 Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 10 / 15
  11. 11. Indicator factors Equal attribute values  Assumption: The more attributes of a trace and its events from both logs are equal, the higher the chance they match 16 JAN 12:00 17 JC 14 14:00 17 JAN 12:10 18 JC 15 14:10 18 JAN 12:20 19 JC 16 14:20 19 JAN 12:30 1A JC 17 14:30 20 JAN 12:40 1B JC 18 14:40 21 JAN 12:50 1C JC 19 14:50 Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 11 / 15
  12. 12. Test results Simulated data (300-400 msec on standard laptop)  Benefit of controllable parameters, known solution  Correct number of linked traces in all tests  Perfect results for same trace id and up to 50% noise, worse results for higher overlap of traces Real data (6-10 min on standard laptop)  Correct number of linked traces in all tests  Almost perfect results for same trace id and up to 50% noise, worse results for higher overlap Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 12 / 15
  13. 13. New approach Rule Based Merger  User has to configure rules for linking traces  Rule = relationship between attributes in both logs  Events of linked traces are merged chronologically “Merge all traces where attribute A of the trace in log 1 equals attribute B of any event in the trace in log 2” Select attributes, contexts and operator Research focus: suggesting merging rules Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 13 / 15
  14. 14. New approach Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 14 / 15
  15. 15. Contact information Jan Claes jan.claes@ugent.be http://processmining.ugent.be Twitter: @janclaesbelgium Ghent University, Faculty of Economics and Business Administration Jan Claes for EIS 2011 Department of Management Information and Operations Management 15 / 15

Slides of my presentation at EIS conference, 31 October 2011, Delft, NL

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