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Scalable SPARQL Querying of Large RDF Graphs
| Citations: | 66 - 1 self |
BibTeX
@MISC{Huang_scalablesparql,
author = {Jiewen Huang and Daniel J. Abadi and Kun Ren},
title = {Scalable SPARQL Querying of Large RDF Graphs},
year = {}
}
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Abstract
The generation of RDF data has accelerated to the point where many data sets need to be partitioned across multiple machines in order to achieve reasonable performance when querying the data. Although tremendous progress has been made in the Semantic Web community for achieving high performance data management on a single node, current solutions that allow the data to be partitioned across multiple machines are highly inefficient. In this paper, we introduce a scalable RDF data management system that is up to three orders of magnitude more efficient than popular multi-node RDF data management systems. In so doing, we introduce techniques for (1) leveraging state-of-the-art single node RDF-store technology (2) partitioning the data across nodes in a manner that helps accelerate query processing through locality optimizations and (3) decomposing SPARQL queries into high performance fragments that take advantage of how data is partitioned in a cluster.
Keyphrases
large rdf graph scalable sparql querying multiple machine rdf data high performance fragment scalable rdf data management system locality optimization reasonable performance high performance data management tremendous progress sparql query current solution semantic web community state-of-the-art single node rdf-store technology query processing data across node many data set single node
