XML视图的代数增量维护

IF 2.2 2区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
A. Bonifati, M. Goodfellow, I. Manolescu, Domenica Sileo
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引用次数: 16

摘要

在查询XML文档时,物化视图可以带来重要的性能优势。在出现XML文档更改时,需要更新物化视图以忠实地反映更改的文档。在这项工作中,我们提出了一种代数方法,用于将源更新传播到用强大的XML树模式形式化表达的XML物化视图。我们的方法在两个重要方面不同于该地区最先进的技术。首先,它依赖于面向集合的代数运算,与之前基于节点的方法形成对比。其次,它利用了XML存储和XML查询计算引擎的最新特性,特别是XML结构标识符和相关的结构连接算法。我们提出了用于确定如何将更新传播到视图的算法,并通过一系列实验强调了我们的方法相对于现有算法的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Algebraic incremental maintenance of XML views
Materialized views can bring important performance benefits when querying XML documents. In the presence of XML document changes, materialized views need to be updated to faithfully reflect the changed document. In this work, we present an algebraic approach for propagating source updates to XML materialized views expressed in a powerful XML tree pattern formalism. Our approach differs from the state-of-the-art in the area in two important ways. First, it relies on set-oriented, algebraic operations, to be contrasted with node-based previous approaches. Second, it exploits state-of-the-art features of XML stores and XML query evaluation engines, notably XML structural identifiers and associated structural join algorithms. We present algorithms for determining how updates should be propagated to views, and highlight the benefits of our approach over existing algorithms through a series of experiments.
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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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