Slides of my presentation at BPI workshop at BPM conference, 29 August 2011, Clermont-Ferrand, FR

1. Merging Computer Log Files for Process Mining:An Artificial Immune System Technique Jan Claes and Geert Poels http://processmining.ugent.be

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 Only As-Is Only (correctly) recorded information Process Mining

3. Keynote BPI 2010, Michael Zur Muehlen Process Controlling Business Activity Monitoring Process Intelligence Event Detection & Correlation Decision Making Main focus point of current BPI research Deserves more focus in BPI research BPI 2010, Keynote Michael Zur Muehlen http://www.slideshare.net/mzurmuehlen/bu-5236080

4. 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 Process Mining steps M M M A M A A M M Manual task Analysts needed in most cases A Automated task Less human involvement needed

5. Merging log files My research:Merging log files

6. Merging log files 2. Merge chronologically 1. Find links 3. Add unlinked traces 4. Put in new log file

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

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

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

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

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 JC 14 14:00 17 17 JAN 12:10 JC 15 14:10 18 18 JAN 12:20 JC 16 14:20 19 19 JAN 12:30 JC 17 14:30 1A 20 JAN 12:40 JC 18 14:40 1B 21 JAN 12:50 JC 19 14:50 1C

12. Indicator factors Extra trace & Missing trace Assumption: A trace from one log has more chance to match with only one trace from the other log Extra trace: Negative if trace is linked with multiple traces in other log Missing trace: Negative if trace is not linked

13. Indicator factors Time difference Assumption: For a certain trace t in one log the trace in the other log that starts sooner after t has a higher chance to match More difficult when traces overlap 16 17 17 JAN 12:00 JC 10 11:45 18 18 JAN 12:10 JC 11 11:55 19 19 JAN 12:20 JC 12 12:05 1A 20 JAN 12:30 JC 13 12:15 1B JAN 12:40 JC 14 12:25 21 1C JAN 12:50 JC 15 12:35

15. Step 2 give feedback on individual scores: user can change weights and restart? Step 3 present best solution per factor: let user choose which factor dominates based on factor score feedback ? Step 4 provide other ways for user to feed algorithm with his insights

17. Further research plans Refining merging technique Quest for optimal indicators and weights is continuous effort (based on experiences from case studies) Implementation optimisation (speed, memory usage, scalability) is continuous effort Validation (case studies)

18. Questions Do you agree that combined set of logical assumptions can be strong indicator (stronger than individual assumptions)? Any feedback on the used factors? Any other factors that should be included? Any concerns about performance and scalability?

19. Contact information Jan Claes jan.claes@ugent.be http://processmining.ugent.be Twitter: @janclaesbelgium