There is an emerging demand on efficiently archiving and (temporal) querying different versions of evolving semantic Web data. As novel archiving systems are starting to address this challenge, foundations/standards for benchmarking RDF archives are needed to evaluate its storage space efficiency and the performance of different retrieval operations. To this end, we provide theoretical foundations on the design of data and queries to evaluate emerging RDF archiving systems. Then, we instantiate these foundations along a concrete set of queries on the basis of a real-world evolving dataset. Finally, we perform an empirical evaluation of various current archiving techniques and querying strategies on this data. Our work comprises – to the best of our knowledge – the first benchmark for querying evolving RDF data archives.
Dr. Javier D. Fernández is a postdoctoral research fellow under an FWF (Austrian Science funds) Lise-Meitner grant. He holds a PhD in Computer Science by the University of Valladolid (Spain), and the University of Chile (Chile), performing this double diploma thanks to an Erasmus Mundus grant. His thesis addressed efficient management of Big Semantic Data, proposing a binary RDF representation for scalable publishing, exchanging and consumption in the Web of Data. Before he joined the WU, he worked at the Ontology Engineering Group (OEG) at Universidad Politécnica de Madrid (Spain), and the Dipartimento di Ingegneria Informatica Automatica e Gestionale at Sapienza Università di Roma (Italy). His current research focuses on efficient Linked Data access, RDF compression and archiving and querying dynamic Linked Data. Other areas of interest are open data, data and knowledge representation, RDF streaming and, in general, algorithms and succinct data structures for the emerging information retrieval needs in Big Data management.