语义web查询的静态分析和优化

IF 2.2 2区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Database Systems Pub Date : 2013-11-01 DOI:10.1145/2500130
Andrés Letelier, Jorge Pérez, R. Pichler, Sebastian Skritek
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引用次数: 38

摘要

静态分析是查询优化中的一项基本任务。在本文中,我们将研究SPARQL的静态分析和优化技术,SPARQL是查询语义Web数据的标准语言。我们特别感兴趣的是SPARQL中的可选特性。它在语义Web数据管理中是至关重要的,在语义Web数据管理中,数据源本质上是不完整的,用户通常对查询的部分答案感兴趣。这个特性是SPARQL中最复杂的构造函数之一,也是使SPARQL语言有别于传统查询语言(如关系连接查询)的一个特性。我们关注的是一类设计良好的SPARQL查询,这类查询在文献中被认为是语言的一个片段,在查询求值方面具有良好的属性。我们首先提出了SPARQL查询的树表示,称为模式树,它捕获了一类设计良好的SPARQL图形模式。在其他结果中,我们提出了一些可用于将模式树转换为简单范式的规则,并研究了等价和包含。我们还研究了这类查询的求值和枚举问题。
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Static analysis and optimization of semantic web queries
Static analysis is a fundamental task in query optimization. In this article we study static analysis and optimization techniques for SPARQL, which is the standard language for querying Semantic Web data. Of particular interest for us is the optionality feature in SPARQL. It is crucial in Semantic Web data management, where data sources are inherently incomplete and the user is usually interested in partial answers to queries. This feature is one of the most complicated constructors in SPARQL and also the one that makes this language depart from classical query languages such as relational conjunctive queries. We focus on the class of well-designed SPARQL queries, which has been proposed in the literature as a fragment of the language with good properties regarding query evaluation. We first propose a tree representation for SPARQL queries, called pattern trees, which captures the class of well-designed SPARQL graph patterns. Among other results, we propose several rules that can be used to transform pattern trees into a simple normal form, and study equivalence and containment. We also study the evaluation and enumeration problems for this class of queries.
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来源期刊
ACM Transactions on Database Systems
ACM Transactions on Database Systems 工程技术-计算机:软件工程
CiteScore
5.60
自引率
0.00%
发文量
15
审稿时长
>12 weeks
期刊介绍: Heavily used in both academic and corporate R&D settings, ACM Transactions on Database Systems (TODS) is a key publication for computer scientists working in data abstraction, data modeling, and designing data management systems. Topics include storage and retrieval, transaction management, distributed and federated databases, semantics of data, intelligent databases, and operations and algorithms relating to these areas. In this rapidly changing field, TODS provides insights into the thoughts of the best minds in database R&D.
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