Abstract
Nowadays, a leading instance of big data is represented by Web data that lead to the definition of so-called big Web data. Indeed, extending beyond to a large number of critical applications (e.g., Web advertisement), these data expose several characteristics that clearly adhere to the well-known 3V properties (i.e., volume, velocity, variety). Resource Description Framework (RDF) is a significant formalism and language for the so-called Semantic Web, due to the fact that a very wide family of Web entities can be naturally modeled in a graph-shaped manner. In this context, RDF graphs play a first-class role, because they are widely used in the context of modern Web applications and systems, including the emerging context of social networks. When RDF graphs are defined on top of big (Web) data, they lead to the so-called large-scale RDF graphs, which reasonably populate the next-generation Semantic Web. In order to process such kind of big data, MapReduce, an open source computational framework specifically tailored to big data processing, has emerged during the last years as the reference implementation for this critical setting. In line with this trend, in this paper, we present an approach for efficiently implementing traversals of large-scale 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 speeds-up the analysis of RDF graphs with respect to competitor approaches. Experimental results clearly support our contributions.
Highlights
Big data describe 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
In line with this trend, in this paper, we present an approach for efficiently implementing traversals of large-scale Resource Description Framework (RDF) graphs over MapReduce that is based on the Breadth
In this paper, we focus on the problem of computing traversals of RDF graphs, being computing traversals of such graphs very relevant in this area, e.g., for RDF graph analysis and mining over large-scale data sets
Summary
Big data describe 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. Our approach adopts the Breadth First Search (BFS) strategy for visiting (RDF) graphs to be decomposed and processed according to the MapReduce framework This with the goal of taking advantages from the powerful run-time support offered by MapReduce, reducing the computational overheads due to manage large RDF graphs efficiently. This approach is conceptually-sound, as similar initiatives have been adopted in the vest of viable solutions to the problem of managing large-scale sensor-network data over distributed environments (e.g., [16,17]), like for the case of Cloud infrastructures and their integration with sensor networks.
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