Query Optimization of Distributed RDF Data Based on MapReduce

Yan Qin Zhang; Jing Bin Wang

doi:10.4028/www.scientific.net/AMM.441.970

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 441Query Optimization of Distributed RDF Data Based...

Query Optimization of Distributed RDF Data Based on MapReduce

Abstract:

As the development of the semantic web, RDF data set has grown rapidly, thus causing the query problem of massive RDF. Using distributed technique to complete the SPARQL (Simple Protocol and RDF Query Language) Query is a new way of solving the large amounts of RDF query problem. At present, most of the RDF query strategies based on Hadoop have to use multiple MapReduce jobs to complete the task, resulting in waste of time. In order to overcome this drawback, MRQJ (using MapReduce to query and join) algorithm is proposed in the paper, which firstly uses a greedy strategy to generate join plan, then only one MapReduce job should be created to get the query results in SPARQL query execution. Finally, a contrast experiment on the LUBM (Lehigh University Benchmark) test data set is conducted, the results of which show that MRQJ method has a great advantage in the case that the query is more complicated.

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Periodical:

Applied Mechanics and Materials (Volume 441)

Pages:

970-973

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.441.970

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Online since:

December 2013

Authors:

Yan Qin Zhang, Jing Bin Wang

Keywords:

Hadoop, MapReduce, RDF, SPARQL Query

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