Query Performance Appraisal using SPARQL & Map Reduce Technique on Web Semantics

The Semantic Web is an emerging technology which aims at making data across the globe semantically connected. The data is represented in a very simple statement like construct having a subject, predicate and an object. This can be visualized as a graph with the subject and the object as nodes and the predicate as an edge connecting the two nodes. When many statements like these are collected together they forms an RDF graph. There are RDF query languages to query such data, and SPARQL is one of them. According to the SP 2 Bench performance benchmarks, the SPARQL queries are very slow for RDF data with millions of triples. Hence, we aim to develop a Query Performance Appraisal using SPARQL & Map Reduce Technique on Web Semantics model of parallelization and hypothesize that this system will outperform the scalability and performance reported by the SP 2 Bench. We extend ARQ, an open source SPARQL query engine provided by the Jena framework, to work with the Hadoop Map Reduce framework and implement distributed SPARQL query processing. This thesis provides the detailed implementation and algorithmic details of our work. We contribute two novel methods to optimize RDF query engine which exploits document indexes and a join preprocessing technique. The experimental results show the merits and demerits of using Map Reduce for distributed RDF query processing and provide us a clear path for future work.