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FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION




                 Merging Event Logs in ProM
                                           Jan Claes
                                       Ghent University
                                http://processmining.ugent.be




Faculty of Economics and Business Administration                                             Jan Claes for TUe 2012
Department of Management Information and Operations Management                                     6 February, 2012
Merging Event Logs




                                                       ?
      Multiple event logs                       ProM plugin      Merged event log

Faculty of Economics and Business Administration                        Jan Claes for TUe 2012
Department of Management Information and Operations Management                           2 / 21
Merging Event Logs




1. Find links      2. Merge chronologically            3. Add unlinked traces   4. Put in new log file
Faculty of Economics and Business Administration                                     Jan Claes for TUe 2012
Department of Management Information and Operations Management                                        3 / 21
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




Faculty of Economics and Business Administration                          Jan Claes for TUe 2012
Department of Management Information and Operations Management                             4 / 21
FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION




                                 1. Genetic Algorithm




Faculty of Economics and Business Administration                                             Jan Claes for TUe 2012
Department of Management Information and Operations Management                                     6 February, 2012
1. Genetic Algorithm




                                                     SEL cross-over
                            RAND       fitness                             MUT
                                                     POP
                             POP                          mutation         POP




                                     Selection              Reproduction




Faculty of Economics and Business Administration                                 Jan Claes for TUe 2012
Department of Management Information and Operations Management                                    6 / 21
1. Genetic Algorithm

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)




Faculty of Economics and Business Administration                  Jan Claes for TUe 2012
Department of Management Information and Operations Management                     7 / 21
1. Genetic Algorithm

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
Faculty of Economics and Business Administration                 Jan Claes for TUe 2012
Department of Management Information and Operations Management                    8 / 21
FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION




                         2. Artificial Immune system




Faculty of Economics and Business Administration                                             Jan Claes for TUe 2012
Department of Management Information and Operations Management                                     6 February, 2012
2. Artificial Immune System


                                                                 Immune cells
                                                                  (type B-cell)




                                               Antigen
                         Antibodies
                         (receptor)




Faculty of Economics and Business Administration                                  Jan Claes for TUe 2012
Department of Management Information and Operations Management                                    10 / 21
2. Artificial Immune System

       HIGH                                  HIGH                                   HIGH


                                                                  mutations
                 INIT               sorted                CLONE               MUT             EDIT
                 POP                 POP                   POP                POP             POP

RAND
 POP                                         LOW                                    LOW
                                                                         Affinity maturation
         Initial population             Clonal selection            Hypermutation Receptor editing

                                                         SEED
       LOW



 Faculty of Economics and Business Administration                                    Jan Claes for TUe 2012
 Department of Management Information and Operations Management                                      11 / 21
2. Artificial Immune System

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)
Faculty of Economics and Business Administration                          Jan Claes for TUe 2012
Department of Management Information and Operations Management                            12 / 21
2. Artificial Immune System

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

Faculty of Economics and Business Administration                 Jan Claes for TUe 2012
Department of Management Information and Operations Management                   13 / 21
FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION




                                         3. Rule Based




Faculty of Economics and Business Administration                                             Jan Claes for TUe 2012
Department of Management Information and Operations Management                                     6 February, 2012
3. Rule Based

Automatic merging is not transparant
 (how good is the merging result?)
Previous algorithms are (too) slow
My 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
Faculty of Economics and Business Administration                 Jan Claes for TUe 2012
Department of Management Information and Operations Management                   15 / 21
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


Faculty of Economics and Business Administration                 Jan Claes for TUe 2012
Department of Management Information and Operations Management                   16 / 21
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



Faculty of Economics and Business Administration                 Jan Claes for TUe 2012
Department of Management Information and Operations Management                   17 / 21
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
Faculty of Economics and Business Administration                 Jan Claes for TUe 2012
Department of Management Information and Operations Management                   18 / 21
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

Faculty of Economics and Business Administration                 Jan Claes for TUe 2012
Department of Management Information and Operations Management                   19 / 21
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
Faculty of Economics and Business Administration                 Jan Claes for TUe 2012
Department of Management Information and Operations Management                   20 / 21
Contact information




                                          Jan Claes
                                          jan.claes@ugent.be

                                          http://processmining.ugent.be
                                          Twitter: @janclaesbelgium
                                          Pav D8.a (until February 10)




Faculty of Economics and Business Administration                          Jan Claes for TUe 2012
Department of Management Information and Operations Management                            21 / 21

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ProM 2012

  • 1. FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION Merging Event Logs in ProM Jan Claes Ghent University http://processmining.ugent.be Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 6 February, 2012
  • 2. Merging Event Logs ? Multiple event logs ProM plugin Merged event log Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 2 / 21
  • 3. Merging Event Logs 1. Find links 2. Merge chronologically 3. Add unlinked traces 4. Put in new log file Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 3 / 21
  • 4. 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 Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 4 / 21
  • 5. FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION 1. Genetic Algorithm Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 6 February, 2012
  • 6. 1. Genetic Algorithm SEL cross-over RAND fitness MUT POP POP mutation POP Selection Reproduction Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 6 / 21
  • 7. 1. Genetic Algorithm 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) Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 7 / 21
  • 8. 1. Genetic Algorithm 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 Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 8 / 21
  • 9. FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION 2. Artificial Immune system Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 6 February, 2012
  • 10. 2. Artificial Immune System Immune cells (type B-cell) Antigen Antibodies (receptor) Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 10 / 21
  • 11. 2. Artificial Immune System HIGH HIGH HIGH mutations INIT sorted CLONE MUT EDIT POP POP POP POP POP RAND POP LOW LOW Affinity maturation Initial population Clonal selection Hypermutation Receptor editing SEED LOW Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 11 / 21
  • 12. 2. Artificial Immune System 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) Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 12 / 21
  • 13. 2. Artificial Immune System 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 Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 13 / 21
  • 14. FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION 3. Rule Based Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 6 February, 2012
  • 15. 3. Rule Based Automatic merging is not transparant (how good is the merging result?) Previous algorithms are (too) slow My 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 Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 15 / 21
  • 16. 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 Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 16 / 21
  • 17. 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 Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 17 / 21
  • 18. 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 Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 18 / 21
  • 19. 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 Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 19 / 21
  • 20. 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 Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 20 / 21
  • 21. Contact information Jan Claes jan.claes@ugent.be http://processmining.ugent.be Twitter: @janclaesbelgium Pav D8.a (until February 10) Faculty of Economics and Business Administration Jan Claes for TUe 2012 Department of Management Information and Operations Management 21 / 21