Cluster-Based Join for Geographically Distributed Big RDF Data
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Date
2019-08-29
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Citation of Original Publication
F. Yang, A. Crainiceanu, Z. Chen and D. Needham, "Cluster-Based Join for Geographically Distributed Big RDF Data," 2019 IEEE International Congress on Big Data (BigDataCongress), Milan, Italy, 2019, pp. 170-178. doi: 10.1109/BigDataCongress.2019.00037; URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8818183&isnumber=8818170
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Subjects
Abstract
Federated RDF systems allow users to retrieve data
from multiple independent sources without needing to have all the
data in the same triple store. The performance of these systems
can be poor for large and geographically distributed RDF data
where network transfer costs are high. This paper introduces
CBTP, a novel join algorithm that takes advantage of network
topology to decrease the cost of processing SPARQL queries in
a geographically distributed environment. Federation members
are grouped in clusters, based on the network communication
cost between the members, and the bulk of the join processing is
pushed to the clusters. We use an overlap list to efficiently compute join results from triples in different clusters. We implement
our algorithms in OpenRDF Sesame federated framework and
use Apache Rya triple store instances as federation members.
Experimental evaluation results show the advantages of our
approach over existing techniques.