On Generation of Time-based Label Refinements
This addresses the challenge of enabling effective process mining in smart home environments for insights into habits and assisted living, though it is an incremental step as it automates a previously manual refinement process.
The paper tackles the problem of uninformative process models in smart home event data by proposing an automated framework for generating time-based label refinements, resulting in more specific and insightful process models as demonstrated on real-life data.
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.