论文标题
基于K2-Trees重新访问紧凑型RDF商店
Revisiting compact RDF stores based on k2-trees
论文作者
论文摘要
我们提出了一个新的紧凑型表示形式，以在主内存中有效存储和查询大型RDF数据集。我们的建议称为BMATRIX，基于K2-Tree，该数据结构设计为以压缩方式表示二进制矩阵，旨在改善以前最先进的替代方案的结果，尤其是在具有相对较大谓词的数据集中。我们介绍了我们的技术，以及对基本K2树的一些改进，可以应用于我们的解决方案，以增强压缩。旗舰RDF数据集DBPEDIA的实验结果表明，我们的建议比现有选择更好，同时产生竞争性的查询时间，尤其是在最频繁的三重模式以及具有未结合谓词的查询中，我们在其中超越了现有的解决方案。
We present a new compact representation to efficiently store and query large RDF datasets in main memory. Our proposal, called BMatrix, is based on the k2-tree, a data structure devised to represent binary matrices in a compressed way, and aims at improving the results of previous state-of-the-art alternatives, especially in datasets with a relatively large number of predicates. We introduce our technique, together with some improvements on the basic k2-tree that can be applied to our solution in order to boost compression. Experimental results in the flagship RDF dataset DBPedia show that our proposal achieves better compression than existing alternatives, while yielding competitive query times, particularly in the most frequent triple patterns and in queries with unbound predicate, in which we outperform existing solutions.
