Abstract
Resource Description Framework (RDF) model owing to its flexible structure is increasingly being used to represent Linked data. The rise in amount of Linked data and Knowledge graphs has resulted in an increase in the volume of RDF data. RDF is used to model metadata especially for social media domains where the data is linked. With the plethora of RDF data sources available on the Web, scalable RDF data management becomes a tedious task. In this paper, we present MuSe—an efficient distributed RDF storage scheme for storing and querying RDF data with Hadoop MapReduce. In MuSe, the Big RDF data is stored at two levels for answering the common triple patterns in SPARQL queries. MuSe considers the type of frequently occuring triple patterns and optimizes RDF storage to answer such triple patterns in minimum time. It accesses only the tables that are sufficient for answering a triple pattern instead of scanning the whole RDF dataset. The extensive experiments on two synthetic RDF datasets i.e. LUBM and WatDiv, show that MuSe outperforms the compared state-of-the art frameworks in terms of query execution time and scalability.
Highlights
IntroductionThe Linked data can be understood and accessed and; is represented by the Semantic Web [2]
Semantic Web is an outcome of the vision of W3C of a ‘Web of Linked data’ [1]
We have carried out extensive experiments on two popular Resource Description Framework (RDF) benchmark datasets i.e. Lehigh University Benchmark (LUBM) and Waterloo SPARQL Diversity Test Suite (WatDiv) to verify the efficiency and scalability of Multi‐Level Big RDF Storage Scheme (MuSe) and compared it with the state-of-the-art SHARD and PigSPARQL frameworks
Summary
The Linked data can be understood and accessed and; is represented by the Semantic Web [2]. Semantic Web provides ease of access to all information available on the World Wide Web (WWW) and represents it in a format that is understandable to both humans and machines. The Semantic Web is being put to good use for information retrieval [3]. The technologies like Web Ontology Language (OWL), RDF, and SPARQL Protocol and RDF Query Language (SPARQL) empower Linked data [4, 5]. Semantic Web has established RDF as the standard model for data interchange. The flexible nature of RDF is a result of its underlying graph-based model that makes it a popular and standard choice for data interchange on the Semantic Web. RDF is a key data representation
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