Changes, planned or unexpected, are common during the execution of real-life processes. Detecting these changes is a must for optimizing the performance of organizations running such processes. Most of the algorithms present in the state-of-the-art focus on the detection of sudden changes, leaving aside other types of changes. In this article, we will focus on the automatic detection of gradual drifts, a special type of change, in which the cases of two models overlap during a period of time. The proposed algorithm relies on conformance checking metrics to carry out the automatic detection of the changes, performing also a fully automatic classification of these changes into sudden or gradual. The approach has been validated with a synthetic dataset consisting of 120 logs with different distributions of changes, getting better results in terms of detection and classification accuracy, delay, and change region overlapping than the main state-of-the-art algorithms.

Predictive monitoring of business processes is a subfield of process mining that aims to predict, among other things, the characteristics of the next event or the sequence of the next events. Although multiple approaches based on deep learning have been proposed, mainly recurrent neural networks and convolutional neural networks, none of them really exploit the structural information available in process models. This paper proposes an approach that simultaneously learns spatio-temporal information from both the event log and the process model by combining recurrent neural networks with graph convolutional networks. Thus, common patterns from process models, such as loops or parallels, can be learned while avoiding overwriting information during the encoding phase. An experimental evaluation of real-life event logs shows that our approach is more consistent and outperforms the current state-of-the-art approaches.