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Predictive business process monitoring aims to
predict how an ongoing process instance will unfold up to
its completion, thereby facilitating proactively responding to
anticipated problems. Recurrent Neural Networks (RNNs), a
special form of deep learning techniques, gain interest as a
prediction technique in BPM. However, non-sequential control
flows may make the prediction task more difficult, because
RNNs were conceived for learning and predicting sequences of
data. Based on an industrial dataset, we provide experimental
results comparing different alternatives for considering non-
sequential control flows. In particular, we consider cycles and
parallelism for business process prediction with RNNs.