“lasagna”-like, process model.[]{data-label="fig:lasagna"}](lasagna){width="25.00000%"}

Several methods have been explored within the process mining field that address the challenge of abstracting low-level events to higher level events ([@Bose2009; @Gunther2010; @Dongen2010]). Existing event abstraction methods rely on unsupervised learning techniques to abstract low-level into high-level events by clustering together groups of low-level events into one high-level event. However, using unsupervised learning introduces two new problems. First, it is unclear how to label high-level events that are obtained by clustering low-level events. Current techniques require the user / process analyst to provide high-level event labels themselves based on domain knowledge, or generate long labels by concatenating the labels of all low-level events incorporated in the cluster. However, long concatenated labels quickly become unreadable for larger clusters, and it is far from trivial for a user to come up with sensible labels manually. In addition, unsupervised learning approaches for event abstraction give no guidance with respect to the desired level of abstraction. Many existing event abstraction methods contain one or more parameters to control the degree in which events are clustered into higher level events. Finding the right level of abstraction providing meaningful results is often a matter of trial-and-error.

In some cases, training data 