Optimal Query Processing in Semantic Web using Cloud Computing

Semantic web technologies can be useful for maintaining data in the Cloud. It adds new data and metadata to existing web documents. This extension of web documents to data will enable the web to be processed automatically by machines. To do this RDF is used to turn basic web data into structured data. In semantic technology large RDF graphs will be used. It poses significant challenges for the storage and retrieval of RDF graphs. So a framework can be built using Hadoop to store and retrieve large numbers of RDF triples. We proposed a sophisticated query model to answer the frequent queries. It will cache statistics for frequent queries and uses dynamic programming to exploit the statistics. It also proposes the history maintenance module to provide updated history information. We used Hadoop�s Map reduce framework to answer the query and SPARQL query language to retrieve the data from RDF storage.