Augmenting Business Process Model Elements With End-User Feedback

Abstract

COVID-19 has imposed unprecedented restrictions on society which has compelled organizations to work ambidextrously. Consequently, organizations need to continuously monitor the performance of their business process and improve them. To facilitate that, this study has put forth the idea of augmenting business process models with end-user feedback and proposed a machine learning based approach (AugProMo) to automatically identify correspondences between end-user feedback and elements of process models. Furthermore, we have generated three valuable resources, process models, feedback corpus and gold standard benchmark correspondences. Also, 2880 experiments are performed to identify the most effective combination of word embeddings, data balancing techniques, feature vectors and machine learning techniques. The study concludes that the proposed approach is effective for augmenting business process models with end-user feedback by identifying correspondences between them.