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SPARQL Query Processing System over Scalable Triple Data using SparkSQL Framework
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 4,  2016, pp.450-459
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.4.450
 Title & Authors
SPARQL Query Processing System over Scalable Triple Data using SparkSQL Framework
Jeon, MyungJoong; Hong, JinYoung; Park, YoungTack;
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Every year, RDFS data tends further toward scalability; hence, the manner of SPARQL processing needs to be changed for fast query. The query processing method of SPARQL has been studied using a scalable distributed processing framework. Current studies indicate that the query engine based on the scalable distributed processing framework i.e., Hadoop(MapReduce) is not suitable for real-time processing because of the repetitive tasks; in addition, it is difficult to construct a query engine based on an In-memory Distributed Query engine, because distributed structure on the low-level is required to be considered. In this paper, we proposed a method to construct a query engine for improving the speed of the query process with the mass triple data. The query engine processes the query of SPARQL using the SparkSQL, which is an In-memory based, distributed query processing framework. SparkSQL is a high-level distributed query engine that facilitates existing SQL statement. In order to process the SPARQL query, after generating the Algebra Tree using Jena, the Algebra Tree is required to be translated to Spark Algebra Tree for application in the Spark system, and construction of the system that generated the SparkSQL query. Furthermore, we proposed the design of triple property table based on DataFrame for more efficient query processing in the Spark system. Finally, we verified the validity through comparative evaluation with the query engine, which is the existing distributed processing framework.
in-memory based distributed query engine;RDFS;SPARQL;spark;SparkSQL;sempala;
 Cited by
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