Implementing BFS-based Traversals of RDF Graphs over MapReduce Efficiently

Big data describes data sets that grow so large that they become unpractical to be processed by traditional tools like database management systems, content management systems, advanced statistical analysis software, and so forth. The reason why they came into the attention of the research community is that the infrastructure to handle these data sets has become more affordable due to Cloud Computing and MapReduce based open-source frameworks. Moreover the effectiveness of analysis on such data sets is supported by Semantic Web technologies, which employ the Resource Description Framework (RDF) model to represent data via a graph-shaped representation. In this paper we present an approach for efficiently implementing traversals of RDF graphs over MapReduce that is based on the Breadth First Search (BFS) strategy for visiting (RDF) graphs to be decomposed and processed according to the MapReduce framework. We demonstrate how such implementation speedsup the analysis of RDF graphs with respect to competitor approaches. Experimental results clearly support our contribution.