Analysis of Big Data Technologies and Method - Query Large Web Public RDF Datasets on Amazon Cloud Using Hadoop and Open Source Parsers

Extremely large datasets found in Big Data projects are difficult to work with using conventional databases, statistical software, and visualization tools. Massively parallel software, such as Hadoop, running on tens, hundreds, or even thousands of servers is more suitable for Big Data challenges. Additionally, in order to achieve the highest performance when querying large datasets, it is necessary to work these datasets at rest without preprocessing or moving them into a repository. Therefore, this work will analyze tools and techniques to overcome working with large datasets at rest. Parsing and querying will be done on the raw dataset - the untouched Web Data Commons RDF files. Web Data Commons comprises five billion pages of web pages crawled from the Internet. This work will analyze available tools and appropriate methods to assist the Big Data developer in working with these extremely large, semantic RDF datasets. Hadoop, open source parsers, and Amazon Cloud services will be used to data mine these files. In order to assist in further discovery, recommendations for future research will be included.