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The authors present D-SPARQ, a distributed RDF query engine that combines the MapReduce processing framework with a NoSQL distributed data store, and MongoDB. The performance of processing SPARQL queries mainly depends on the efficiency of handling the join operations between the RDF triple patterns. Their system features two unique characteristics that enable efficiently tackling this challenge: identifying specific patterns of the input queries that enable improving the performance by running different parts of the query in a parallel mode. Using the triple selectivity information for re-ordering the individual triples of the input query within the identified query patterns.
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