Storing and Indexing RDF Data in a Column-Oriented DBMS

Xin Wang, Shuyi Wang, Pufeng Du, Zhiyong Feng
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引用次数: 8

Abstract

Effcient RDF data management is an essential factor in realizing the Semantic Web vision. However, most existing RDF storage schemes based on row-store relational databases are constrained in terms of efficiency and scalability. In this paper, we propose an RDF storage scheme that implements sextuple indexing for RDF triples using a column-oriented DBMS. To evaluate the performance of our approach, large-scale datasets upto 13 million triples are generated and benchmark queries that cover important RDF join patterns are devised. The experimental results show that our approach outperforms the row-oriented DBMS approach by upto an order of magnitude and is even competitive to the best state-of-the-art native RDF store.
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在面向列的DBMS中存储和索引RDF数据
高效的RDF数据管理是实现语义Web愿景的关键因素。然而,大多数现有的基于行存储关系数据库的RDF存储模式在效率和可伸缩性方面受到限制。在本文中,我们提出了一种RDF存储方案,该方案使用面向列的DBMS实现RDF三元组的六元索引。为了评估我们的方法的性能,我们生成了多达1300万个三元组的大规模数据集,并设计了涵盖重要RDF连接模式的基准查询。实验结果表明,我们的方法比面向行DBMS方法的性能高出一个数量级,甚至可以与最先进的原生RDF存储相媲美。
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