high-level events. Conditional Random Fields are trained of the annotated traces to create a probabilistic mapping from low-level events to high-level events. This mapping, once obtained, can be applied to the unannotated traces in order to estimate the corresponding high-level event for each low-level event (its *label* attribute). Often sequences of low-level events in the traces with high-level annotations will have the same *label* attribute. We make the working assumption that multiple high-level events are executed in parallel. This enables us to interpret a sequence of identical *label* attribute values as a single instance of a high-level event. To obtain a true high-level log, we *collapse* sequences of events with the same value for the *label* attribute into two events with this value as *concept* name, where the first event has a *lifecycle* ’start’ and the second has the *lifecycle* ’complete’. Table \[tab:collapse\] illustrates this collapsing procedure through an input and output event log.

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  Case   Time:timestamp        Concept:name            label
  ------ --------------------- ----------------------- -----------------
  1      03/11/2015 08:45:23   Medicine cabinet        Taking medicine
  1      03/11/2015 08:46:11   Dishes & cups cabinet   Taking medicine
  1      03/11/2015 08:46:45   Water                   Taking medicine
  1      03/11/2015 08:47:59   Dishes & cups cabinet   Eating
  1      03/11/2015 08:47:89   Dishwasher              Eating
  1      03/11/2015 