The Community for Technology Leaders
2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW) (2013)
Brisbane, Australia Australia
Apr. 8, 2013 to Apr. 12, 2013
ISBN: 978-1-4673-5303-8
pp: 1-6
K. Hose , Dept. of Comput. Sci., Aalborg Univ., Aalborg, Denmark
R. Schenkel , Max Planck Inst. for Inf., Saarbrucken, Germany
ABSTRACT
With the increasing popularity of the Semantic Web, more and more data becomes available in RDF with SPARQL as a query language. Data sets, however, can become too big to be managed and queried on a single server in a scalable way. Existing distributed RDF stores approach this problem using data partitioning, aiming at limiting the communication between servers and exploiting parallelism. This paper proposes a distributed SPARQL engine that combines a graph partitioning technique with workload-aware replication of triples across partitions, enabling efficient query execution even for complex queries from the workload. Furthermore, it discusses query optimization techniques for producing efficient execution plans for ad-hoc queries not contained in the workload.
INDEX TERMS
Resource description framework, Query processing, Servers, Optimization, Parallel processing, Distributed databases,
CITATION
K. Hose, R. Schenkel, "WARP: Workload-aware replication and partitioning for RDF", 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), vol. 00, no. , pp. 1-6, 2013, doi:10.1109/ICDEW.2013.6547414
191 ms
(Ver 3.3 (11022016))