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Process mining enables the reconstruction and evaluation of businessprocesses based on digital traces in IT systems. An increasingly importanttechnique in this context is process prediction. Given a sequence of events ofan ongoing trace, process prediction allows forecasting upcoming events orperformance measurements. In recent years, multiple process predictionapproaches have been proposed, applying different data processing schemes andprediction algorithms. This study focuses on deep learning algorithms sincethey seem to outperform their machine learning alternatives consistently.Whilst having a common learning algorithm, they use different datapreprocessing techniques, implement a variety of network topologies and focuson various goals such as outcome prediction, time prediction or control-flowprediction. Additionally, the set of log-data, evaluation metrics and baselinesused by the authors diverge, making the results hard to compare. This paperattempts to synthesise the advantages and disadvantages of the proceduraldecisions in these approaches by conducting a systematic literature review.

A systematic literature review on state-of-the-art deep learning methods for process prediction