BibTeX:

@article{IJIRSTV1I7062,
     title={A Survey on an Efficient Query Processing and Analysis on Big Data (RDF) Using Map Reduce},
     author={PRAVINSINH MORI, A. R. Kazi and Sandip Chauhan},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={1},
     number={7},
     pages={142--146},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV1I7062.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

In Big Data analysis Semantic Web Large data analysis is an important topic in cloud computing that are used particularly using RDF. This has positioned the issue of scalable data processing techniques for RDF as a vital issue in the Semantic Web research community. The RDF data model is a fine-grained model representing associations as binary relations. Thus, responding queries over RDF data requires several join operations to reassemble related data. While MapReduce based processing is emerging as the Map and Reduce paradigm for processing large scale data. In addition, most of the existing techniques for optimizing RDF data handing out do not transfer well to the MapReduce model and often require significant lead time for pre-processing. MapReduce is a programming structure in cloud computing to compute data analysis in parallel.We suggests three concepts. First data are filtered primary according to the query statements. Second, the filtered data are sent to its equal worker according to the join expression for advanced level parallelism. Each worker then performs the corresponding join operation after acceptance of the filtered data. Finally, we aggregate the result by using aggregate functions specific in the select clause.

Map Reduce, MJR Framework, RDF, Query Optimization, RDF Graph Pattern Matching, Hadoop