Ali Hasnain

TL;DR: The concept of FAIR implementation considerations is introduced to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations.

TL;DR: This work performs extensive experiments to compare state-of-the-art SPARQL endpoint federation systems using the comprehensive performance evaluation framework Fed- Bench and extends FedBench to mirror a highly distributed data environment and assess the behavior of existing systems by using the same performance criteria.

TL;DR: This work proposes LargeRDFBench, a billion-triple benchmark for SPARQL query federation which encompasses real data as well as real queries pertaining to real bio-medical use cases and indicates that current federation systems seem unable to deal with real queries that involve processing large intermediate result sets or lead to large result sets.

TL;DR: An overview of the federation framework infrastructure is given and a comparison of existing SPARQL federation frameworks are compared to highlight shortcomings in existing frameworks, which the authors hope helps spawning new research directions.

TL;DR: The efficient cataloguing approach of the federated query processing system ’BioFed’, the triple pattern wise source selection and the semantic source normalisation forms the core to the solution and facilitates efficient query generation for data access and provides basic provenance information in combination with the retrieved data.