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Slides of my presentation at BPI workshop at BPM conference, 29 August 2011, Clermont-Ferrand, FR
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Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 201110 April 2023
FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION
Merging Computer Log Files for Process Mining:An Artificial Immune System Technique
Jan Claes and Geert Poelshttp://processmining.ugent.be
Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 20112 / 18
Process Mining
Processes are supported by IT systemsIT systems record actual process dataProcess 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
Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 20113 / 18
Keynote BPI 2010, Michael Zur Muehlen
BPI 2010, Keynote Michael Zur Muehlen http://www.slideshare.net/mzurmuehlen/bu-5236080
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
Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 20114 / 18
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 actionM
Process Mining steps
A
MM
M
MA
A
MA
Manual task Analysts needed in most cases
Automated task Less human involvement needed
Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 20115 / 18
Merging log files
My research:Merging log files
Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 20116 / 18
Merging log files
1. Find links 2. Merge chronologically 3. Add unlinked traces 4. Put in new log file
Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 20117 / 18
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 Department of Management Information and Operations Management
Jan Claes for BPI@BPM 20118 / 18
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 Department of Management Information and Operations Management
Jan Claes for BPI@BPM 20119 / 18
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 Department of Management Information and Operations Management
Jan Claes for BPI@BPM 201110 / 18
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)
161718192021
101214161820
Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 201111 / 18
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
JAN 12:00JAN 12:10JAN 12:20JAN 12:30JAN 12:40JAN 12:50
JC 14 14:00JC 15 14:10JC 16 14:20JC 17 14:30JC 18 14:40JC 19 14:50
161718192021
1718191A1B1C
Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 201112 / 18
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
Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 201113 / 18
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
JAN 12:00JAN 12:10JAN 12:20JAN 12:30JAN 12:40JAN 12:50
JC 10 11:45JC 11 11:55JC 12 12:05JC 13 12:15JC 14 12:25JC 15 12:35
161718192021
1718191A1B1C
Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 201114 / 18
User interaction
Step 1 let user adapt parameters & weightsStep 2 give feedback on individual scores:
user can change weights and restart? Step 3 present best solution per factor:
let user choose which factor dominatesbased on factor score feedback
? Step 4 provide other ways for user to feed algorithm with his insights
Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 201115 / 18
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 tracesReal 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 Department of Management Information and Operations Management
Jan Claes for BPI@BPM 201116 / 18
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)
Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 201117 / 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?
Ghent University, Faculty of Economics and Business Administration Department of Management Information and Operations Management
Jan Claes for BPI@BPM 201118 / 18
Contact information
http://processmining.ugent.beTwitter: @janclaesbelgium