A Graph Partitioning Approach to Distributed RDF Stores

With growing of Semantic Web data, especially RDF data, managing large RDF dataset on a single machine does not scale well. Previous work has explored how to distribute RDF triples to multiple machines. However due to inefficient dataset partitioning used by these solutions, the performance of distr...

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Vydáno v:2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications s. 411 - 418
Hlavní autoři: Rui Wang, Chiu, K.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2012
Témata:
Benchmark testing
communication cost
dataset partition
Distributed databases
distributed RDF store
Indexes
Parallel processing
parallelism
Peer to peer computing
RDF graph
Resource description framework
Resource management
triplestore
ISBN:1467316318, 9781467316316
ISSN:2158-9178
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Shrnutí:With growing of Semantic Web data, especially RDF data, managing large RDF dataset on a single machine does not scale well. Previous work has explored how to distribute RDF triples to multiple machines. However due to inefficient dataset partitioning used by these solutions, the performance of distributed store system is significantly affected. In this paper, we proposed a promising approach that utilized the graph nature of RDF datasets to minimize relations between partitions after dataset partitioning, and optimized system design based on it. As shown in our experiments, our approach can effectively reduce communication cost of query-processing messages, balance size of partitions compared with other approaches, and enhance parallelism through independent sub-querying.
ISBN:1467316318
9781467316316
ISSN:2158-9178
DOI:10.1109/ISPA.2012.60