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Implementing BFS-based Traversals of RDF Graphs over MapReduce Efficiently

机译:在MapReduce上有效实现基于BFS的RDF图遍历

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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.
机译:大数据描述的数据集变得如此之大,以至于无法由数据库管理系统,内容管理系统,高级统计分析软件等传统工具进行处理。它们之所以引起研究界的注意,是因为基于云计算和基于MapReduce的开源框架使处理这些数据集的基础结构变得更加负担得起。此外,语义Web技术支持对此类数据集进行分析的有效性,该技术采用资源描述框架(RDF)模型通过图形表示法表示数据。在本文中,我们提出了一种基于在MapReduce上有效实现RDF图遍历的方法,该方法基于要根据MapReduce框架进行分解和处理的访问(RDF)图广度优先搜索(BFS)策略。我们演示了这种实现如何相对于竞争对手的方法来加快对RDF图的分析。实验结果显然支持了我们的贡献。

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