low-level sensor signals are mapped to high-level activities from a human behavior perspective. The input data in this setting are continuous time series from sensors. Change points in these time series are triggered by low-level activities like *opening/closing the fridge door*, and the annotations of the higher level events (e.g. *cooking*) are often obtained through manual activity diaries. In contrast to unsupervised event abstraction, the annotations in supervised event abstraction provide guidance on how to label higher level events and guidance for the target level of abstraction.

Event Abstraction as a Sequence Labeling Task {#sec:features}
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In this section we describe an approach to supervised abstraction of events based on Conditional Random Fields. Additionally, we describe feature functions on XES event logs in a general way by using XES extensions. Figure \[fig:overview\] provides a conceptual overview of the supervised event abstraction method. The approach takes two inputs, 1) a set of annotated traces, which are traces where the high-level event that a low-level event belongs to (the *label* attribute of the low-level event) is known for all low-level events in the trace, and 2) a set of unannotated traces, which are traces where the low-level events are not mapped to 