Explainable Predictive Process Monitoring
Abstract
Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring with explanation capabilities, so that not only the what but also the why is reported when predicting generic KPIs like remaining time, or activity execution. We use the game theory of Shapley Values to obtain robust explanations of the predictions. The approach has been implemented and tested on real-life benchmarks, showing for the first time how explanations can be given in the field of predictive business process monitoring.
Cite
@article{arxiv.2008.01807,
title = {Explainable Predictive Process Monitoring},
author = {Riccardo Galanti and Bernat Coma-Puig and Massimiliano de Leoni and Josep Carmona and Nicolò Navarin},
journal= {arXiv preprint arXiv:2008.01807},
year = {2020}
}
Comments
Galanti, R., Coma-Puig, B., de Leoni, M., Carmona, J., Navarin, N.: Explainable Predictive Process Monitoring, the International Conference on Process Mining (ICPM 2020), IEEE Computational Intelligence Society, 2020. Article accepted for publication