a state-transition matrix. From the observed prefixes and their next events, a state transition matrix is built. When a running case has reached a state not contained in the state-transition matrix, its similarity to observed traces is computed using string edit distance. The prediction is made from the most similar observed case. Evaluating the approach on two datasets from a telecommunications company, Le et al. (2012) report accuracies in predicting the next event of up to 25% and 70% for their two datasets. The approach described by Lakshmanan et al. (2015) and Unuvar et al. (2016) consists of five steps. First, a process model is mined from existing logs. For each XOR split in the model, a decision tree is mined from case data. These trees are then used to compute the state transition probabilities for a HMM that is specific to the running case that is to be predicted. This HMM is then used to predict the probabilities of the following event. The approach is evaluated on simulated data. After training the decision trees for the HMM on one half of the traces (training set), each trace in the test set is cut into a prefix and postfix at a random point. For each prefix, the log-likelihood of observing the corresponding postfix is reported. The approach described by Ceci et al. (2014) uses sequence mining to identify frequent trace prefixes. For each prefix, a regression model is trained to predict re- maining time to completion and a decision tree is trained to predict the next event. The algorithm identifies the appropriate prefix of the running case to choose the regression and decision tree model for predicting remaining completion time and the next event. The experimental evaluation uses two datasets, yielding prediction precision values for the next event of approximately 65% on one dataset and ap- proximately 50% on the other, depending on the type of decision tree used and the frequency threshold for prefixes. RegPFA (Breuker et al., 2016) uses a probabilistic finite automaton (PFA) in- 4