Scalable Semantic Web Data Management Using Vertical Partitioning
Scalable Semantic Web Data Management Using Vertical Partitioning
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发表时间：
2007-09
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通讯作者：
D. Abadi;Adam Marcus;S. Madden;Katherine J. Hollenbach
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作者：
D. Abadi;Adam Marcus;S. Madden;Katherine J. Hollenbach
RDF数据的有效管理是实现语义Web愿景的一个重要因素。随着语义Web技术应用于实际应用，性能和可伸缩性问题变得越来越紧迫。在本文中，我们研究了为什么目前的RDF数据管理解决方案的规模差的原因，并探讨这些方法的基本可扩展性的限制。我们回顾了提高RDF数据库性能的最新技术，并考虑了最近的建议，“属性表”。“然后，我们从实践和经验上讨论为什么这种解决方案具有不受欢迎的特征。作为改进，我们提出了一种替代方案：垂直分区的RDF数据。我们比较了垂直分区与现有技术的性能查询产生的基于Web的RDF浏览器在一个大规模（超过50万个三元组）的图书馆数据目录。我们的研究结果表明，垂直分区模式实现了类似的性能的属性表技术，而更简单的设计。此外，如果使用面向列的DBMS（专门为垂直分区情况设计的数据库）而不是面向行的DBMS，则可以观察到另一个数量级的性能改进，查询时间从几分钟下降到几秒。
Efficient management of RDF data is an important factor in realizing the Semantic Web vision. Performance and scalability issues are becoming increasingly pressing as Semantic Web technology is applied to real-world applications. In this paper, we examine the reasons why current data management solutions for RDF data scale poorly, and explore the fundamental scalability limitations of these approaches. We review the state of the art for improving performance for RDF databases and consider a recent suggestion, "property tables." We then discuss practically and empirically why this solution has undesirable features. As an improvement, we propose an alternative solution: vertically partitioning the RDF data. We compare the performance of vertical partitioning with prior art on queries generated by a Web-based RDF browser over a large-scale (more than 50 million triples) catalog of library data. Our results show that a vertical partitioned schema achieves similar performance to the property table technique while being much simpler to design. Further, if a column-oriented DBMS (a database architected specially for the vertically partitioned case) is used instead of a row-oriented DBMS, another order of magnitude performance improvement is observed, with query times dropping from minutes to several seconds.