Towards Optimizing Ontology-Based Data Federation: Performance Insights from Experimental Studies

Ontology-Based Data Federation (OBDF) is a recently introduced framework that utilizes virtual knowledge graphs for real-time, unified data querying across diverse data sources. This approach significantly enhances data integration efficiency. As part of a PhD research, we aim to further investigate and optimize the performance of OBDF. To this end, we have set up and conducted extensive experiments with OBDF using three representative data federation systems, which allow for a comprehensive assessment of the framework and the extensions developed in this thesis. This paper provides a recap of OBDF and details the experimental work conducted and its results, paving the way for further OBDF refinements to be developed during the PhD thesis.