$l$, is valued with the multinoulli-estimated probability of label $l$ given the n-gram of the last $n$ event resources/roles/groups.

### Time extension

In terms of time, there are several potentially existing patterns. A certain high-level event might for example be concentrated in a certain parts of a day, of a week, or of a month. This concentration can however not be modeled with a single Gaussian distribution, as it might be the case that a high-level event has high probability to occur in the morning or in the evening, but low probability to occur in the afternoon in-between. Therefore we use a Gaussian Mixture Model (GMM) to model the probability of a high-level label $l$ given the timestamp. Bayesian Information Criterion (BIC) [@Schwarz1978] is used to determine the number of components of the GMM, which gives the model an incentive to not combine more Gaussians in the mixture than needed. A GMM is estimated on training data, modeling the probabilities of each label based on the time passed since the start of the day, week or month. A time extension based feature function with two parameters, $t\in\{day,week,month,\dots\}$ and label $l$, is valued with the GMM-estimated probability of label $l$ given 