Explainable Predictive Monitoring of Temporal Measures of Business Processes
Ilya Verenich · Queensland University of Technology · 2018
This thesis explores data-driven, predictive approaches to monitor business process performance. These approaches allow process stakeholders to prevent or mitigate potential performance issues or compliance violations in real time, as early as possible. To help users understand the rationale for the predictions and build trust in them, the thesis proposes two techniques for explainable predictive process monitoring: one based on deep learning, the other driven by process models. This is achieved by decomposing a prediction into its elementary components. The techniques are compared against state-of-the-art baselines and a trade-off between accuracy and explainability of the predictions is evaluated.