Publication
Explainable Process Predictions (xPP): A Holistic Framework and Applications
Nijat Mehdiyev
In: Claudio Di Ciccio; Benoît Depaire; Jochen De Weerdt; Chiara Di Francescomarino; Jorge Munoz-Gama (Hrsg.). ICPM 2020. International Conference on Process Mining (ICPM-2020), 2nd, October 4-9, Padua, Italy, Pages 17-18, Vol. 2703, CEUR, 2020.
Abstract
Business process prediction also referred to as predictive process monitoring or predictive business process management is a branch of process mining that pursues the objective to predict the target of interest by using the activities from the process traces. Recently, several studies have been conducted to explore the applicability of various machine learning approaches for different problems in the process prediction context such as next event prediction, process outcome prediction, prediction of service level agreement violations, remaining time prediction, risk prediction, cost prediction, prediction of activity delays etc. The recent research also suggests that the black-box machine learning approaches especially deep learning methods provide superior results for process prediction problems compared to conventional approaches. However, these opaque, nontransparent models lack the capabilities to provide explanations about their reasoning trace or delivered outcomes. This in turn introduces the barriers to operationalizing data-driven decisionmaking since the users tend not to use the outcomes by such artificial advice givers due to the lack of understanding or justification.