Abstract
This chapter looks at the extent to which the semantic-based process mining approach of this book supports the conceptual analysis of the events logs and resultant models. Qualitatively, the chapter leverages the use case study of the research learning process domain to determine how the proposed method support the discovery, monitoring, and enhancement of the real-time processes through the abstraction levels of analysis. Also, the chapter quantitatively assesses the level of accuracy of the classification process to predict behaviours of unobserved instances within the underlying knowledge base. Overall, the work looks at the implications of the semantic-based approach, validation of the classification results, and their influence compared to other existing benchmark techniques/algorithms used for process mining.
Reference27 articles.
1. W3C. (2012). Web Ontology Language (OWL). Oxford, UK: OWL Working Group.
2. Decision quality enhancement in minimum-based possibilistic classification for numerical data.;K.Baati;Proceedings of the 8th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016),2017)
3. Bishop, B. (1999). WSML Reasoner. Boston, MA: IRIS Reasoner - SOA4All.
4. Translating Process Mining Results into Intelligible Business Information