Efficient RDF Query Processing using Multidimensional Indexing

Akrivi Vlachou
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引用次数: 1

Abstract

Efficient management of RDF data is important due to the increasing number of data sources available in RDF, which fosters interoperability and enables data integration and interlinking. An RDF store maintains data in the form of triples, subject (S), property (P), object (O). A common approach for storing RDF data is to follow a so-called "one-triples-table" approach, where all triples are stored in a single table. For efficient data access, multiple one-dimensional clustered indexes (for example B+trees) are built, practically replicating and storing the triples in different sort orders, thus enabling efficient random access based on any subset of SPO. An issue that is largely overlooked in related research is that since triples are 3-dimensional, a natural alternative is to store the triples in a (single) multidimensional index. In this paper, we explore this direction and study the performance of evaluating RDF queries over triples stored in a multidimensional index. We propose an efficient algorithm for processing star group pattern queries on top of a multidimensional index. By means of an extensive evaluation on real-life data sets, we demonstrate the merits of our indexing scheme and the efficiency of our algorithm.
使用多维索引的高效RDF查询处理
RDF数据的有效管理非常重要,因为RDF中可用的数据源数量在不断增加,这促进了互操作性并支持数据集成和互连。RDF存储以三元组、主题(S)、属性(P)、对象(O)的形式维护数据。存储RDF数据的一种常用方法是遵循所谓的“one-triples-table”方法,其中所有三元组都存储在单个表中。为了实现高效的数据访问,需要构建多个一维聚类索引(例如B+树),实际上是以不同的排序顺序复制和存储三元组,从而实现基于SPO的任意子集的高效随机访问。在相关研究中很大程度上被忽视的一个问题是,由于三元组是三维的,因此自然的替代方法是将三元组存储在(单个)多维索引中。在本文中,我们探索了这个方向,并研究了在多维索引中存储的三元组上评估RDF查询的性能。提出了一种基于多维索引处理星型组模式查询的高效算法。通过对实际数据集的广泛评估,我们证明了我们的索引方案的优点和我们的算法的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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