Characteristic sets: Accurate cardinality estimation for RDF queries with multiple joins

Thomas Neumann, G. Moerkotte
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引用次数: 251

Abstract

Accurate cardinality estimates are essential for a successful query optimization. This is not only true for relational DBMSs but also for RDF stores. An RDF database consists of a set of triples and, hence, can be seen as a relational database with a single table with three attributes. This makes RDF rather special in that queries typically contain many self joins. We show that relational DBMSs are not well-prepared to perform cardinality estimation in this context. Further, there are hardly any special cardinality estimation methods for RDF databases. To overcome this lack of appropriate cardinality estimation methods, we introduce characteristic sets together with new cardinality estimation methods based upon them. We then show experimentally that the new methods are-in the RDF context-highly superior to the estimation methods employed by commercial DBMSs and by the open-source RDF store RDF-3X.
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特征集:对具有多个连接的RDF查询进行精确的基数估计
准确的基数估计对于成功的查询优化至关重要。这不仅适用于关系dbms,也适用于RDF存储。RDF数据库由一组三元组组成,因此可以将其视为具有三个属性的单个表的关系数据库。这使得RDF非常特殊,因为查询通常包含许多自连接。我们表明关系dbms在这种情况下没有做好执行基数估计的准备。此外,对于RDF数据库几乎没有任何特殊的基数估计方法。为了克服缺乏合适的基数估计方法的问题,我们引入了特征集以及基于特征集的新的基数估计方法。然后,我们通过实验证明,在RDF上下文中,这些新方法比商业dbms和开源RDF存储RDF- 3x所使用的估计方法要优越得多。
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