No abstract

COVID-19 has imposed unprecedented restrictions on the society which has compelled the organizations to work ambidextrously. Consequently, the organizations need to go 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. In particular, we have generated three valuable resources, process models, feedback corpus and gold standard benchmark correspondences. Furthermore, 2880 experiments are performed to identify the most effective combination of word embeddings, feature vectors, data balancing and machine learning techniques. The study concludes that the proposed approach is effective for augmenting business process models with end-user feedback.

In computer networks, file transfer methods are used as a tool to exchange files among computers. Efficiency of a file transfer method is generally measured in terms of bandwidth usage, integrity, security, scalability and fault tolerance. We have critically reviewed some state-of-the-art patent file transfer methods to know about their shortcomings and strengths for different scenarios. We analyzed that none of them is up to the mark in terms of all parameters of efficiency and there is a great need of research in this area. After a critical analysis, some methods for the improvement have also been proposed in this study.