We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques.
Refinements of sensor level event labels suggested by domain experts have been shown to enable discovery of more precise and insightful process models.
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.
In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log.
Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes.
Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i. e. subsets of possible events are taken into account to create so-called local process models.
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).
We show that when process discovery algorithms are only able to discover an unrepresentative process model from a low-level event log, structure in the process can in some cases still be discovered by first abstracting the event log to a higher level of granularity.
We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective.
The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining.