activity. A feature based on both the lifecycle and the time extension, with a label parameter $l$ and lifecycle $c$, is valued with the GMM-estimated probability of label $l$ given the time between the current event and lifecycle $c$. Bayesian Information Criterion (BIC) [@Schwarz1978] is again used to determine the number of components of the GMM.

Evaluating High-level Event Predictions for Process Mining Applications
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Existing approaches in the field of activity recognition take as input time windows where each time window is represented by a feature vector that describes the sensor activity or status during that time window. Hence, evaluation methods in the activity recognition field are window-based, using evaluation metrics like the percentage of correctly classified time slices [@Tapia2004; @Kasteren2007; @Kasteren2008]. There are two reasons to deviate from this evaluation methodology in a process mining setting. First, our method operates on events instead of time windows. Second, the accuracy of the resulting high level sequences is much more important for many process mining techniques (e.g. process discovery, conformance checking) than the accuracy of predicting each individual minute of the day.

We use *Levenshtein similarity* that expresses the degree in which two traces are similar using a metric based 