A Multi-Query Optimization Algorithm Using Map Reduce
Abstract
The need for storing statements about web resources lead to the emergence of the semantic web technology. A World Wide Web Consortium (W3C) standard for storing the semantic web data is Resource Description Framework (RDF). The existing frameworks do not provide scalability for large RDF graphs. This paper focuses on the problem of multi-query optimization of semantic web data. A scalable framework for storing RDF graphs is designed using Hadoop Distributed file system and the problem of multi-query optimization in the perspective of SPARQL is revisited in this research. Algorithms for multi-query optimization is proposed and query execution is done through map reduce programming to get the final result of optimized query. Experiments were conducted on the LUBM benchmark dataset. The algorithm is executed on Jena data store and the Hadoop framework. The extent to which the algorithm is efficient and scalability is tested and the results are documented. Keywords: hadoop, map reduce, query optimization, resource description framework, semantic webReferences
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