Explainable Predictive Process Monitoring: A User Evaluation
Explainability is motivated by the lack of transparency of black-box Machine Learning approaches, which do not foster trust and acceptance of Machine Learning algorithms. This also happens in the...
How do I update my model? On the resilience of Predictive Process Monitoring models to change
Existing well investigated Predictive Process Monitoring techniques typically construct a predictive model based on past process executions, and then use it to predict the future of new ongoing...
Nirdizati: an Advanced Predictive Process Monitoring Toolkit
Predictive Process Monitoring 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...
Incremental Predictive Process Monitoring: How to Deal with the Variability of Real Environments
A characteristic of existing predictive process monitoring techniques is to first construct a predictive model based on past process executions, and then use it to predict the future of new...
Combining Natural Language Processing Approaches for Rule Extraction from Legal Documents
Making Sense of Temporal Event Data:A Framework for Comparing Techniques for the Discovery of Discriminative Temporal Patterns
Nirdizati Light: A Modular Framework for Explainable Predictive Process Monitoring
A ProM Operational Support Provider for Predictive Monitoring of Business Processes
Explainability in Predictive Process Monitoring: When Understanding Helps Improving
Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles
Recent papers have introduced a novel approach to explain why a Predictive Process Monitoring (PPM) model for outcome-oriented predictions provides wrong predictions. Moreover, they have shown how...