用于RDF分析的高效OLAP操作

Elham Akbari Azirani, François Goasdoué, I. Manolescu, Alexandra Roatis
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引用次数: 23

摘要

RDF是语义Web的领先数据模型,SPARQL 1.1等专门的查询语言具有特别的聚合特性,允许从RDF图中提取信息。在[1]中引入了一个用于RDF数据分析处理的框架,其中分析模式和分析查询(多维数据集)被完全重新设计为异构的、语义丰富的RDF图。在这种新颖的分析设置中,我们考虑以下优化问题:如何重用给定RDF分析查询(多维数据集)的物化结果,以便计算另一个多维数据集的答案。我们为这些立方体转换提供了基于视图的重写算法,并通过实验证明了它们的实际意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient OLAP operations for RDF analytics
RDF is the leading data model for the Semantic Web, and dedicated query languages such as SPARQL 1.1, featuring in particular aggregation, allow extracting information from RDF graphs. A framework for analytical processing of RDF data was introduced in [1], where analytical schemas and analytical queries (cubes) are fully re-designed for heterogeneous, semantic-rich RDF graphs. In this novel analytical setting, we consider the following optimization problem: how to reuse the materialized result of a given RDF analytical query (cube) in order to compute the answer to another cube. We provide view-based rewriting algorithms for these cube transformations, and demonstrate experimentally their practical interest.
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