Abstract: in predictive process monitoring. Different approaches that learn from event logs, e.g., relying on an existing representation of the process or leveraging machine learning approaches, have been proposed in the literature to tackle this problem. Machine learning-based techniques have shown superiority over other techniques with respect to the accuracy of the prediction as well as freedom from knowledge about the underlying process models generating the logs. However, all proposed approaches only learn from complete traces. This might cause delays in starting new training cycles as usually process instances last over a long time that could even reach months or years. In this thesis, we propose a machine learning approach that can also accept and learn from incomplete (ongoing) traces. Using a time-aware survival analysis technique, we can train a neural network to predict the most likely remaining cycle time of a running case. This approach is evaluated on different real-life datasets and is compared with a state-ofthe- art baseline. Results show that our approach, in most cases, is able to outperform the baseline approach with a simple model architecture and less training time. The approach is further enhanced to learn from trace level - fixed - features as well as the events-related features. We empirically proved that trace-level features enhance the prediction power of the model using a real-life dataset.