Query Processing for RDF Graphs using Hadoop Mapreduce Framework in the Heterogeneous Environment

Semantic web technology represents data in a standardized way such that the data can be retrieved and understood by both humans and machines.W3C developed Resource Description Framework (RDF) standard for encoding metadata and other knowledge on the Semantic Web.With the evolution of Semantic web technologies, the storage and retrieval of large RDF graphs poses significant challenges. Current frameworks do not address these challenges. In this paper we describe how Hadoop mapreduce framework is used to store and retrieve large numbers of RDF triples. We describe a scheme to store RDF/XML data in Hadoop Distributed File System as N-Triple. Hadoop's MapReduce framework is used to answer the SPARQL Protocol and RDF Query Language (SPARQL) queries. We have compared the performance of Hadoop's MapReduce framework with the results of Apache Jena framework and the results show that the Hadoop's MapReduce Framework outperforms the Apache Jena framework for complex queries and fulfils the essentials of semantic web such as scalability and high speed response time.