Abstract
The proliferation of semantic data on the Web requires RDF database systems to constantly improve their scalability and transactional efficiency. At the same time, users are increasingly interested in investigating or visualizing large collections of online data by performing complex analytic queries. This paper introduces a novel database system for RDF data management called dipLODocus , which supports both transactional and analytical queries efficiently. dipLODocus takes advantage of a new hybrid storage model for RDF data based on recurring graph patterns. In this paper, we describe the general architecture of our system and compare its performance to state-of-the-art solutions for both transactional and analytic workloads. © 2011 Springer-Verlag.
Cite
CITATION STYLE
Wylot, M., Pont, J., Wisniewski, M., & Cudré-Mauroux, P. (2011). dipLODocus[RDF] - Short and long-tail rdf analytics for massive webs of data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7031 LNCS, pp. 778–793). https://doi.org/10.1007/978-3-642-25073-6_49
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