HadoopRDF: a scalable semantic data analytical engine

摘要: With the rapid growth of scale semantic data, to handle problem analyzing this large-scale data has become a hot topic. Traditional triple stores deployed on single machine have been proved be effective provide storage and retrieval RDF data. However, scalability is limited cannot billion ever growing triples. On other hand, Hadoop an open-source project which provides HDFS as distributed file system MapReduce computing framework for processing. It perform well large analysis. In paper, we propose, HadoopRDF, combine both worlds (triple Hadoop) scalable analysis service benefits ability support flexible query like SPARQL traditional stores. Experimental evaluation results show effectiveness efficiency approach.