基于XML的多分支模式匹配算法

随着XML文档大量涌现,如何有效地管理和查询XML数据已经成为亟待解决的问题。在数据库中,基于视图的查询重写技术是查询优化的一个重要技术,利用缓存的视图结果回答新查询不需要访问源数据库进行查询,能够节省查询处理时间。本文在研究基于物化视图的查询重写的过程中,给定一个查询和一个视图,首先判断该查询在视图下是否存在查询重写,而重写的存在问题与模式包含问题密切相关,为此,提出使用多分支的模式匹配算法实现对多分支路径的模式匹配,解决重写是否存在的问题。     

数据集成中XML数据查询语义重写

查询重写是数据库研究的一个基本问题，它和查询优化，数据仓库，数据集成，语义缓存等数据库问题密切相关，为提高集成系统的查询效率，系统选择提交频率较高的XML查询物化为中间层视图，用户提交查询后，系统尽可能利用中间视图层中视图，而不是访问数据源来回答查询，这个问题实际可以归结为半结构化查询重写问题，考虑到中间视图层空间的有限性，已有视图应当尽可能回答更多的查询，传统查询重写方法有考虑半结构化数据之间的约束，而根据约束可以等价变换查询，从而提高中间视图层中的表达能力，提出了一种新的半结构化查询重写的方法，该方法在保证算法正确性和完备性的基础上，利用上半结构化数据中的约束，尤其是XML文件中的路径依赖，来增强中间层物化视图的表达能力，理论分析和初步原型实验证明方法的有效性。     

XML递归安全视图的查询重写算法

XML(Extensive Markup Language)安全视图和查询重写是实现访问控制的关键技术.研究了基于XML递归安全视图的查询重写问题,分析了以前有关查询重写的研究工作,提出了一种基于XPath查询语言的能处理递归视图的查询重写算法,并对算法进行了分析,解决了当安全视图中存在递归时把查询进行有效重写的问题,避免了视图的物化和保存.     

基于XPath的XML查询重写算法

XML安全视图和查询重写是实现访问控制的关键技术。研究基于XML递归安全视图的查询重写问题,提出一种基于XPath查询语言、能处理递归视图的查询重写算法,避免了视图的物化和保存。该算法具有较高通用性,实验结果验证了其有效性。     

基于物化视图方法优化数据库查询效率

为提高数据库的查询性能,对查询操作进行优化是非常必要的.优化查询速度的措施主要针对数据库管理系统层与应用层,物化视图是其中的一种方法.本文采用物化视图的方法对数据库查询效率进行优化.实验表明,此方法具有较好的优化效果.     

基于Oracle物化视图的查询性能优化

Oracle物化视图作为由查询结果构成的数据库对象,可以在本地镜像远程数据库的查询结果,从而有效提高查询效率.介绍了物化视图的概念和作用,物化视图的创建和使用,给出了通过物化视图进行数据库设计的案例说明.     

半结构化数据查询重写

查询重写是数据库研究的一个基本问题，它和查询优化，数据仓库，信息集成，语义缓存等问题紧密相关，目前Internet上存在海量的半结构化数据，在信息集成过程中产生了大量半结构化视图，如何利用物化半结构化视图来重写用户查询，减少响应时间成为研究热点问题，上述问题本质上是NP问题，提出了一种半结构化查询重写的新方法，该方法在保证算法正确性和完备性的基础上，利用半结构化数据特点和查询子目标之间的关系，减少了指数空间的查询重写候选方案生成，理论分析表明，它极大地降低了算法的代价。     

利用视图重写技术优化XML查询

规则路径查询，其长度是任意的，这就意味着对数据库的任意多次访问，这样的代价是很昂贵的。我们采用视图重写技术，通过对某些经常使用的路径查询定义视图。从而减少了对某些高频使用的路径查询的重复搜索，最终提高了查询效率。我们的视图不但可以对路径查询进行重写，而且还可以对树查询进行混合重写。我们设计了一个使用动态规划策略实现的视图重写算法。     

Web数据集成系统基于QC模型的物化视图选择

在Web数据集成系统中,物化视图能够有效地减少网络传输代价,提高系统的查询效率．如何选择查询进行物化,使得选中的查询满足集成层的空间限制,同时获取最大物化收益,成为集成系统中一个迫切需要解决的问题．传统方法没有考虑到海量XML查询之间的包含关系,其选择的物化视图中可能包含冗余的信息．针对上述问题,提出了①Web数据集成系统中海量查询集合的QC(query containment)模型,该模型能够捕捉查询之间最常见的包含关系;②基于QC模型的物化视图选择算法,算法考虑了物化视图选择相关的主要因素,包括查询提交的频率、空间代价、查询重写能力和查询结果的完备性,提出了查询位图的物化视图组织方式,从而获取更加合理的物化视图选择方案．实验结果证明了该方法的有效性．     

对查询优化重写QRA算法的一种改进

数据仓库系统中利用物化视图可以提高查询效率,但是,过分使用物化视图重写查询有时不一定能起到提高查询性能这一预期目的.针对QRA算法的不足并结合左深度处理树技术提出一种改进算法,通过实验结果表明由改进算法优化后查询的性能更优.     

Finding an efficient rewriting of OLAP queries using materialized views in data warehouses

OLAP queries involve a lot of aggregations on a large amount of data in data warehouses. To process expensive OLAP queries efficiently, we propose a new method to rewrite a given OLAP query using various kinds of materialized views which already exist in data warehouses. We first define the normal forms of OLAP queries and materialized views based on the selection and aggregation granularities, which are derived from the lattice of dimension hierarchies. Conditions for usability of materialized views in rewriting a given query are specified by relationships between the components of their normal forms. We present a rewriting algorithm for OLAP queries that can effectively utilize materialized views having different selection granularities, selection regions, and aggregation granularities together. We also propose an algorithm to find a set of materialized views that results in a rewritten query which can be executed efficiently. We show the effectiveness and performance of the algorithm experimentally.     

Detecting redundant materialized views in data warehouse evolution

A data warehouse (DW) can be abstractly seen as a set of materialized views defined over a set of remote data sources. A DW is intended to satisfy a set of queries. The views materialized in a DW relate to each other in a complex manner, through common subexpressions, in order to guarantee high query performance and low view maintenance cost. DWs are time varying. As time passes new materialized views are added in order to satisfy new queries, or for performance reasons, while old queries are dropped. The evolution of a DW can result in a redundant set of materialized views. In this paper, we address the problem of detecting redundant materialized views in a given DW view selection, that is, materialized views that can be removed from DW without negatively affecting the query evaluation or the view maintenance process. Using an AND/OR dag representation for multiple queries and views, we first formalize the process of propagating source relation changes to the materialized views by exploiting common subexpressions between views and by using other materialized views that are not affected by these changes. Then, we provide an algorithm for detecting materialized views that are not needed in the process of propagating source relation changes to the DW. We also show how trivially redundant views can be identified in this process. Finally, we use these results to provide a procedure for detecting materialized views that are redundant in a DW. Our approach considers a broad class of views that includes grouping/aggregation views and is not dependent on a specific cost model.     

Incremental Design of a Data Warehouse

A data warehouse (DW) can be seen as a set of materialized views defined over remote base relations. When a query is posed, it is evaluated locally, using the materialized views, without accessing the original information sources. The DWs are dynamic entities that evolve continuously over time. As time passes, new queries need to be answered by them. Some of these queries can be answered using exclusively the materialized views. In general though new views need to be added to the DW.In this paper we investigate the problem of incrementally designing a DW when new queries need to be answered and possibly extra space is allocated for view materialization. Based on an AND/OR dag representation of multiple queries, we model the problem as a state space search problem. We design incremental algorithms for selecting a set of new views to additionally materialize in the DW that: (a) fits in the extra space, (b) allows a complete rewriting of the new queries over the materialized views, and (c) minimizes the combined new query evaluation and new view maintenance cost. Finally, we discuss methods for pruning the search space so that efficiency is improved.     

Materialized view selection using evolutionary algorithm for speeding up big data query processing

For speeding up query processing on Big Data, frequent sub-queries or views may be materialized such that the query processing cost is minimized with optimum cost of maintaining the materialized views and/or queries. Materializing frequent sub-queries and views means that resultant data set of the views reside in the memory of one or more nodes in the cluster, so that it reduces the MapReduce cost, submission and scheduling cost of Distributed File System jobs for query processing. We have defined materialized views as resultant data of frequent sub-queries and aggregation functions of a set of Big Data warehousing queries that are saved for enhancing query performance. The problem is defined as a multi-objective optimization problem for minimizing the total query processing MapReduce cost, MapReduce cost for maintaining the materialized views and the number of views selected for materializing with maximized total size of the views selected. We applied Differential Evolution algorithm and NSGA-II to study their performances for developing a recommendation system for selecting views for materializing in Big Data warehousing.     

Answering queries using views: A survey

The problem of answering queries using views is to find efficient methods of answering a query using a set of previously defined materialized views over the database, rather than accessing the database relations. The problem has recently received significant attention because of its relevance to a wide variety of data management problems. In query optimization, finding a rewriting of a query using a set of materialized views can yield a more efficient query execution plan. To support the separation of the logical and physical views of data, a storage schema can be described using views over the logical schema. As a result, finding a query execution plan that accesses the storage amounts to solving the problem of answering queries using views. Finally, the problem arises in data integration systems, where data sources can be described as precomputed views over a mediated schema. This article surveys the state of the art on the problem of answering queries using views, and synthesizes the disparate works into a coherent framework. We describe the different applications of the problem, the algorithms proposed to solve it and the relevant theoretical results. Received: 1 August 1999 / Accepted: 23 March 2001 Published online: 6 September 2001     

Optimizing XML queries: Bitmapped materialized views vs. indexes

Optimizing queries using materialized views has not been addressed adequately in the context of XML due to the many limitations associated with the definition and usability of materialized views in traditional XML query evaluation models.     

一种扩充语义的实视图重写查询技术

分组聚集查询已成为数据仓库领域研究的核心问题之一,实视图是提高分组聚集查询性能的有效手段。利用维属性间的层次关系,对一般意义上的实视图重写查询进行了扩展,讨论了单一视图重写查询的限制条件,并给出重写方法,在此基础上,提出了一种利用多个实视图重写查询的优化选择算法,并通过实验表明,该算法进一步提高了分组聚集查询效率。     

面向XML物化视图远程增量维护的版本管理技术研究

Web应用中以XML为格式的信息查询通常会受到网络传输速度有限等因素的影响.为了减少XML的物化视图与其数据源之间的一致性维护中所需的网络数据传输开销,提出了一种面向远程的XML物化视图增量维护的方法.这种方法根据多用户的查询请求和数据源更新信息,生成视图维护程序代码,以程序代码的网络迁移代替XML视图的重复查询,有效地减少了网络数据传输量.重点介绍了增量维护过程中处于核心的版本管理控制方法,用于维护不同时刻数据更新所对应的视图更新程序代码,有效地适应了多用户各种物化视图的维护需求,并且给出了物化视图增量维护的系统框架.     

数据仓库环境中可扩展的动态物化视图选择方法

数据仓库通常要对大量的数据进行运算,以精简的结果来回答用户的查询,这一特点使得物化视图技术在数据仓库中尤为重要.然而现有支持物化视图自动选择的方法是静态的,它违背了联机分析处理(OLAP)和决策支持系统(DSS)的动态本质.本文提出了可扩展的动态物化视图方法,通过将整个物化视图选择问题(MVS)分解为三个阶段,降低了问题的复杂度,提高了物化视图的有效性.通过动态调整,物化视图能即时适应查询需求.算法复杂度分析证明了方案的可扩展性.动态调整算法模拟实验验证了方案具有很好的自适应性.