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.
Vienna University of Economics and Business
http://www.wu.ac.at/infobiz/en/
Dr. Javier D. Fernández is a post-doctoral research fellow at Vienna University of Economics and Business (WU). His current research focuses on efficient management of Big Semantic Data, privacy-aware Big Data technologies and archiving and querying dynamic Linked Data.
