A Multi-Query Optimization Algorithm Using Map Reduce
Abstract
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 web
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