ProM 2012

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

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION

Jan Claes for TUe 20128 April 2023

Merging Event Logs in ProMJan Claes

Ghent Universityhttp://processmining.ugent.be


Jan Claes for TUe 20122 / 21

Merging Event Logs

ProM plugin

?Merged event logMultiple event logs



Merging Event Logs

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



Approaches

Genetic Algorithm J. Claes, G. Poels, Integrating Computer Log Files for Process Mining: a Genetic

Algorithm Inspired Technique, in CAiSE 2011 Workshops, LNBIP 83, 2011

Artificial Immune System J. Claes, G. Poels, Merging Computer Log Files for Process Mining: an Artificial

Immune System Technique, in BPM 2011 Workshops, LNBIP 99, 2011

Rule Based J. Claes, G. Poels, Merging Event Logs for Process Mining: A Rule Based Merging

Method and Rule Suggestion Algorithm, to be submitted in 2012




1. Genetic Algorithm




SELPOP

cross-overMUTPOPmutation

RANDPOP

ReproductionSelection

fitness




Fitness function Sum of weighted factor scores per link

• Same trace id (STIi)

• Trace order (TOi) if all start events are in the first log

• Equal attribute values (EAVi)

• Number of linked traces (NLTi)

• Time distance (TDi)




Simplification Population size one Only mutations

Improvements More intelligent start population (not random) More intelligent mutations (improve at least one

factor of the fitness function)Attention

Intensification vs. diversification




2. Artificial Immune system



2. Artificial Immune System

Immune cells(type B-cell)

Antibodies(receptor)

Antigen




HIGH

LOW

INITPOP

HIGH

LOW

CLONEPOP

EDITPOP

SEED

HIGH

LOW

Initial population Hypermutation Receptor editingClonal selectionAffinity maturation

mutations MUTPOP

sortedPOP

RAND POP




Clonal selection Clone the fittest x% solutions (I)

Hypermutation Randomly change each clone The higher the fitness score, the less changes (I)

Receptor editing Take the best y% solutions (I) Add totally random solutions to the set (D)

(I: Intensification, D: Diversification)




Hypermutation Choose ‘random’ indicator factor to improve

• Higher chance to pick factors with positive previous effect Choose random action

• Add link, remove link or alter link Choose random candidate

• From all solutions that would improve with selected action Choose random improvement

• From all possible improvements for selected candidate




3. Rule Based



3. Rule Based

Automatic merging is not transparant(how good is the merging result?)

Previous algorithms are (too) slowMy experience

in most cases it is about finding an attribute value (literally) in a trace of the other log

you need data experts/analyst to get the right data, they mostly have a good idea about the link between two log files



3. Rule Based

Semi-automatic solution Let user configure merging rule based on attribute

values• More transparent• Faster• Includes expert knowledge if available

Help user by suggesting merging rules based on the data in the log



3. Rule Based

Merging rules Merge all traces where…

attribute <select name> from <select container> in the 1st log<select operator> attribute <select name> from <select container> in the 2nd log

E.g. Merge all traces where attribute Trace ID from a trace in the 1st log equals attribute Supplier Reference from event Send goods in the 2nd log



3. Rule Based

<select name>• Contains all possible attribute names available in the log

<select container>• From a trace• From any event in a trace• From a trace or any event in a trace• From event X, From event Y, From event Z, …

<select operator>• equals, is not equal, greater than, greater or equal, …• comes before, comes after



3. Rule Based

Suggesting rules Look at all attribute values in the log Make a rule for every equal match in both logs Count the number of linked traces for every rule Filter rules with only one link Sort such that rule that is closer to 1-to-1 match is

higher in the list• rules that make more or fewer links are lower in the list• if no 1-to-1 rule exist, the ‘best’ rule is still on top



3. Rule Based

Some remarks User can configure rules or select from the

suggestion list Suggestion list is currently limited to equals-rules

but is calculated very fast (order n1 + n2 !) Rules can be combined with And or Or By explicitly selecting rules, the approach is more

transparent Possible use as shortcut for merging logs from

within one system



Contact information

Jan [email protected]

http://processmining.ugent.beTwitter: @janclaesbelgiumPav D8.a (until February 10)