17:10:58   Dishes & cups cabinet   Taking medicine
  1      03/11/2015 17:10:69   Medicine cabinet        Taking medicine
  1      03/11/2015 17:11:18   Water                   Taking medicine

[0.48]{}

  Case   Time:timestamp        Concept:name      Lifecycle:transition
  ------ --------------------- ----------------- ----------------------
  1      03/11/2015 08:45:23   Taking medicine   Start
  1      03/11/2015 08:46:45   Taking medicine   Complete
  1      03/11/2015 08:47:59   Eating            Start
  1      03/11/2015 08:47:89   Eating            Complete
  1      03/11/2015 17:10:58   Taking medicine   Start
  1      03/11/2015 17:11:18   Taking medicine   Complete

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The method described in this section is implemented and available for use as a plugin for the ProM 6 [@Verbeek2010] process mining toolkit and is based on the GRMM [@Sutton2006] implementation of Conditional Random Fields.

We now show for each XES extension how it can be translated into useful feature functions for event abstraction. Note that we do not limit ourselves to XES logs that contain all XES extensions; when a log contains a subset of the extensions, a subset of the feature functions will be available for the supervised learning step. This approach leads to a feature space of unknown size, potentially causing problems related to the curse of dimensionality, therefore we use L1-regularized Conditional Random Fields. L1 regularization causes the vector of feature weights to be sparse, meaning that only a small fraction of 