High throughput indexing for large-scale semantic web data
Distributed RDF data management systems become increasingly important with the growth of the Semantic Web. Currently, several such systems have been proposed, however, their indexing methods meet performance bottlenecks either on data loading or querying when processing large amounts of data. In this work, we propose a high throughout index to enable rapid analysis of large datasets. We adopt a hybrid structure to combine the loading speed of similarsize based methods with the execution speed of graph-based approaches, using dynamic data repartitioning over query workloads. We introduce the design and detailed implementation of our method. Experimental results show that the proposed index can indeed vastly improve loading speeds while remaining competitive in terms of performance. Therefore, the method could be considered as a good choice for RDF analysis in large-scale distributed scenarios.