In complex processes, various events can happen in different sequences. The prediction of the next event activity given an a-priori process state is of importance in such processes. Recent methods leverage deep learning techniques such as recurrent neural networks to predict event activities from raw process logs. However, deep learning techniques cannot efficiently model logical behaviors of complex processes. In this paper, we take advantage of Petri nets as a powerful tool in modeling logical behaviors of complex processes. We propose an approach which first discovers Petri nets from event logs utilizing a recent process mining algorithm. In a second step, we enhance the obtained model with time decay functions to create timed process state samples. Finally, we use these samples in combination with token movement counters and Petri net markings to train a deep learning model that predicts the next event activity. We demonstrate significant performance improvements and outperform the state-of-the-art methods on eight out of nine real-world benchmark event logs in accuracy.
In semi-structured processes, the set of activities that need to be performed, their order and whether additional steps are required are determined by human judgment. There is a growing demand for operational support of such processes during runtime particularly in the form of predictions about the likelihood of future tasks. We address the problem of making predictions for a running instance of a semi-structured process that contains parallel execution paths where the execution path taken by a process instance influences its outcome. In particular, we consider five different models for how to represent an execution trace as a path attribute for training a prediction model. We provide a methodology to determine whether parallel paths are independent, and whether it is worthwhile to model execution paths as independent based on a comparison of the information gain obtained by dependent and independent path representations. We tested our methodology by simulating a marketing campaign as a business process model and selected decision trees as the prediction model. In the evaluation, we compare the complexity and prediction accuracy of a prediction model trained with five different models.