  Process mining is a research field focused on the analysis of event data with
the aim of extracting insights in processes. Applying process mining techniques
on data from smart home environments has the potential to provide valuable
insights in (un)healthy habits and to contribute to ambient assisted living
solutions. Finding the right event labels to enable application of process
mining techniques is however far from trivial, as simply using the triggering
sensor as the label for sensor events results in uninformative models that
allow for too much behavior (overgeneralizing). Refinements of sensor level
event labels suggested by domain experts have shown to enable discovery of more
precise and insightful process models. However, there exist no automated
approach to generate refinements of event labels in the context of process
mining. In this paper we propose a framework for automated generation of label
refinements based on the time attribute of events. We show on a case study with
real life smart home event data that behaviorally more specific, and therefore
more insightful, process models can be found by using automatically generated
refined labels in process discovery.
