A Schematic Analysis on Selective-RDF Database Stores

RDF has gained great interest in both academia and industry as an important language to describe graph data. With the increasing amount of RDF data which is becoming available, efficient and scalable nowadays has become a challenge to achieve the semantic web vision. The RDF model has attracted the attention of the database community and researchers to propose various methods to store and query the RDF data efficiently. However, current RDF database suffer from several problems, like, poor performance behavior for querying RDF data.. This paper provides a comparative analysis made on selective RDF databases storages. It provides a precise study on the various means of having a persistent storage and access of RDF graphs. Recently there has been a major development on initiatives in query processing, access protocols and triple-store technologies. In the evaluation the use of a nonmemory and a non-native store Sesame, a native store Allegro graph and Jena API a main-memory based RDF storage system, specifically designed to support fast semantic association discovery. The framework and applications with the ability to store and to query RDF data are analyzed and investigated. Moreover, this paper gives an overview of the features of techniques for storing RDF data and the main purpose of study is to find suitable storage system to store RDF data.

[1]  Shridevika Maharajan,et al.  Performance of native SPARQL query processors , 2012 .

[2]  H. Kosch,et al.  Evaluation of Current RDF Database Solutions , 2005 .

[3]  W. Campbell,et al.  THE UNIVERSITY OF TEXAS AT DALLAS , 2004 .

[4]  Eric Miller,et al.  An Introduction to the Resource Description Framework , 1998, D Lib Mag..

[5]  Vassilis Christophides,et al.  The RDFSuite: Managing Voluminous RDF Description Bases , 2000 .

[6]  Jeff Heflin,et al.  LUBM: A benchmark for OWL knowledge base systems , 2005, J. Web Semant..

[7]  Vassilis Christophides,et al.  The ICS-FORTH RDFSuite: Managing Voluminous RDF Description Bases , 2001, SemWeb.

[8]  Jeff Heflin,et al.  Choosing the best knowledge base system for large semantic web applications , 2004, WWW Alt. '04.

[9]  Chuen-Tsai Sun,et al.  An educational genetic algorithms learning tool , 2001, IEEE Trans. Educ..

[10]  Marco Furini,et al.  International Journal of Computer and Applications , 2010 .

[11]  Olivier Curé,et al.  Towards a better insight of RDF triples Ontology-guided Storage system abilities , 2013, ArXiv.

[12]  Alisdair Owens,et al.  An Investigation into Improving RDF Store Performance , 2009 .

[13]  Guillaume Blin,et al.  A survey of RDF storage approaches , 2012, ARIMA J..

[14]  Vassilis Christophides,et al.  On Storing Voluminous RDF Descriptions: The Case of Web Portal Catalogs , 2001, WebDB.

[15]  Wolf Siberski,et al.  SLUBM: An Extended LUBM Benchmark for Stream Reasoning , 2013, OrdRing@ISWC.

[16]  K. Bandeen-Roche,et al.  Appendix 1 , 2019, European Journal of Human Genetics.

[17]  Nigel Shadbolt,et al.  Resource Description Framework (RDF) , 2009 .

[18]  Henry Lieberman,et al.  Sesame: An Architecture for Storing and Querying RDF Data and Schema Information , 2005 .