Cluster-Based Join for Geographically Distributed Big RDF Data
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Type of Work9 pages
conference papers and proceedings
Citation of Original PublicationF. 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.
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.
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