Abstract Predictive Process Monitoring (PPM) is a field of Process Mining that aims at predicting how an ongoing execution of a business process will develop in the future using past process executions recorded in event logs. The recent stream of publications in this field shows the need for tools able to support researchers and users in comparing and selecting the techniques that are the most suitable for them. In this paper, we present , a dedicated tool for supporting users in building, comparing and explaining the PPM models that can then be used to perform predictions on the future of an ongoing case. has been constructed by carefully considering the necessary capabilities of a PPM tool and by implementing them in a client-server architecture able to support modularity and scalability. The features of support researchers and practitioners within the entire pipeline for constructing reliable PPM models. The assessment using reactive design patterns and load tests provides an evaluation of the interaction among the architectural elements, and of the scalability with multiple users accessing the prototype in a concurrent manner, respectively. By providing a rich set of different state-of-the-art approaches, offers to Process Mining researchers and practitioners a useful and flexible instrument for comparing and selecting PPM techniques.

Existing well-investigated Predictive Process Monitoring techniques typically construct a predictive model based on past process executions and then use this model to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make Predictive Process Monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and/or exhibit new variant behaviours over time. As a solution to this problem, we evaluate the use of three different strategies that allow the periodic rediscovery or incremental construction of the predictive model so as to exploit new available data. The evaluation focuses on the performance of the new learned predictive models, in terms of accuracy and time, against the original one, and uses a number of real and synthetic datasets with and without explicit Concept Drift. The results provide an evidence of the potential of incremental learning algorithms for predicting process monitoring in real environments.