VERONA: A python library for benchmarking deep learning in business process monitoring

Abstract

Predictive process monitoring is a subfield of process mining that focuses on predicting the future behavior of real-world processes, anticipating constraint violations and bottlenecks, and enabling real-time decision making. Among other machine learning approaches, Deep Learning-based architectures have achieved high levels of prediction accuracy, becoming an increasingly prolific area of research in recent years. However, the variety of datasets, learning techniques, and metrics used makes the comparison of proposals complicated and biased. To address this problem this paper presents VERONA, a Python library designed for the development of the deep learning predictive process monitoring pipeline. Additionally, this library provides a framework for replicating the experimental setup of the state-of-the-art benchmark in the field, enabling streamlined comparison of new approaches and improving the reproducibility of experiments.