A ‘spaghetti model’ is obtained after applying Process Discovery on a flexible, unstructured process:
1
Log is clustered in smaller sub-logs based om common behavoir: cluster 1 capturing 74% of process instances
cluster 2 capturing 11% of process instances
unclustered log model
cluster 3 capturing 4% of process instances
cluster 4 capturing 11% of remaining, non-fitting, low-frequent process instances 2
Cluster characteristics are analysed to build predictive decision tree: cluster 1
cluster 2
cluster 3
cluster 4
3
mean completion time: 3.3 days mean nr. workers involved: 2 involved product types: P201, P202 ... mean completion time: 4.5 days mean nr. workers involved: 5 involved product type: P203 mean completion time: 32.4 days mean nr. workers involved: 12 involved product type: P204
attribute 1 attribute 2
cluster 1
cluster 2
attribute 3
cluster 3
cluster 4
mean completion time: 11.7 days mean nr. workers involved: 7 involved product types: P205, P206, P207
Characteristics of new instances can be predicted:
new process instance
expected completion time: 4.5 days expected amount of involved workers: 5
involved product type: P203 ...
Figuur 6. Grotere complexe eventlogs kunnen geclusterd worden in verschillende sublogs. De geĂŤxtraheerde procesmodellen bieden een overzicht over de verschillende gedragstypes in data die gemakkelijker te begrijpen zijn. De laatste cluster in deze figuur bevat alle procesinstanties die niet in een van de eenvoudigere clusters bevat konden worden en die dus beschouwd kunnen worden als een restcategorie die alle infrequente en zeldzame procesvarianten bevat.
informatie / januari/februari 2014
predicted cluster 2:
43