with high-level target labels of low-level events are available, or can be obtained, for a subset of the traces. In many settings, obtaining high-level annotations for all traces in an event log is infeasible or too expensive. Learning a supervised learning model on the set of traces where high-level target labels are available, and applying that model to other low-level traces where no high-level labels are available, allows us to build a high-level interpretation of a low-level event log, which can then be used as input for process mining techniques.

In this paper we describe a method for supervised event abstraction that enables process discovery from too fine-grained event logs. This method can be applied to any event log where higher level training labels of low level events are available for a subset of the traces in the event log. We start by giving an overview of related work from the activity recognition field in Section \[sec:related\]. In Section \[sec:preliminaries\] we introduce basic concepts and definitions used throughout the rest of the paper. Section \[sec:motivating\_example\] explains the problem of not being able to mine representative process models from low-level data in more detail. In Section \[sec:features\] we describe a method 