  Process mining techniques focus on extracting insight in processes from event
logs. Process mining has the potential to provide valuable insights in
(un)healthy habits and to contribute to ambient assisted living solutions when
applied on data from smart home environments. However, events recorded in smart
home environments are on the level of sensor triggers, at which process
discovery algorithms produce overgeneralizing process models that allow for too
much behavior and that are difficult to interpret for human experts. We show
that abstracting the events to a higher-level interpretation can enable
discovery of more precise and more comprehensible models. We present a
framework for the extraction of features that can be used for abstraction with
supervised learning methods that is based on the XES IEEE standard for event
logs. This framework can automatically abstract sensor-level events to their
interpretation at the human activity level, after training it on training data
for which both the sensor and human activity events are known. We demonstrate
our abstraction framework on three real-life smart home event logs and show
that the process models that can be discovered after abstraction are more
precise indeed.
