Abstract
Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for data analysts to navigate through this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying predictive process monitoring methods. This objective is achieved by systematically reviewing existing work in this area. Starting from about 780 papers retrieved through a keyword-based search from electronic libraries and filtering them according to some exclusion criteria, 55 papers have been finally thoroughly analyzed and classified. Then, the review has been used to develop the value-driven framework that can support researchers and practitioners to navigate through the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques.