Enhancing the quality of predictions in predictive business process monitoring

Abstract

The aim of this thesis is to develop and evaluate methods to enhance the quality of predictions for predictive business process monitoring, focusing on the accuracy and stability of predictions. Three different approaches to increase the accuracy and stability of predictions are suggested. Firstly, to improve the accuracy of predictions on minority classes, different resampling techniques are applied to data samples in event logs and the accuracy of the predictions from these resampling techniques are compared, and new metric that considers different weights of activities is developed. Secondly, the stability of predictions of different data distribution-resampling technique pairs is compared. Lastly, the stability of predictions of different case types in event logs is compared, and a new performance metric that considers different case types and provides balanced predictions is suggested. Besides evaluation using publicly available event logs, a case study is also conducted using a real-life event log from a large hospital in South Korea. © 2019 CEUR-WS. All rights reserved.