数据仓库多源实化视图一致性维护的方法研究

研究了多源单视图下数据仓库实化视图联机维护与查询一致性问题,并对现有算法做了改进.改进后的算法在更改信息中增加时间戳控制数据源端查询的时序,并引入动作列表控制数据仓库端信息提交的顺序,采用补偿思想和应答机制来协调数据源与数据仓库间的数据更新,从而保证了实化视图维护和查询的一致性.     

数据仓库中实化视图选择方法的研究

数据仓库中的视图选择在很大程度上影响数据仓库的查询效率和维护代价,是数据仓库中研究的重要内容.通过对现有的选择实化视图的价值模型进行研究,提出了一个新的视图价值估算模型和视图选择算法--PBPUS算法.该视图选择方法通过在视图选择前进行预处理和使用新的价值估算模型,降低了视图搜索的时间复杂度和维护代价,有效地提高了实化视图的查询效率.     

一种维护分解的并行增量视图维护方法

实化视图维护是指在数据源的原始数据发生改变时,有效地将这种变化反映到数据仓库中,使相应的实化视图得到及时更新.当前的视图维护方法主要以C/S结构为基础,当更新频繁时将会导致数据仓库超载而崩溃.针对这种现状,提出基于维护查询任务进行分解的实化视图并行增量维护P3Sweep算法,算法修正了Sweep算法只能顺序处理的限制,赋予其并行处理能力.对于单个更新,P3Sweep算法通过对维护查询任务进行分解,并行执行左右扫描过程来完成维护演算;对于并发更新,算法通过递归分解方式进行细粒度并行演算.性能计算和实验测试结果表明,该方法增加了系统对更新的吞吐能力,减少了维护的延迟,从维护查询任务本身实施对实化视图维护的优化.     

多数据源数据仓库的一致性维护算法——Strobe算法的改进

数据仓库是一个集成了多个分布式、自治或异构数据源上的信息的数据储藏室,以支持用户的查询和分析。该文介绍了DM3数据仓库实现多数据源实化视图一致性维护的策略,分析了产生视图不一致性的原因和解决办法,以及改进后的一致性维护算法:Strobe算法和T-Strobe算法。     

数据仓库实化视图的联机维护

数据仓库实化视图和联机维护是数据仓库系统维护的一项关键技术,采用这种技术,能够在不影响用户正常业务的情况下,实现数据仓库的实化视图数据的及时更新、联机分析处理（OLAP）作为数据仓库的一个主要应用,在数据仓库实化视图的联机维护的过程中会面临严重的数据不一致问题。为了解决这个问题,本文引入“维护库”（Maintaining Database)的概念,提出基于事务触发的视图维护算法TVM,通过消息应答机制实现实化视图与数据源的数据一致性。     

基于时间戳实化视图动态维护技术的研究

由于源数据的不稳定性,其结构和数据的变化必须及时传播到实体化视图中,以保持实体化视图与源数据变化的一致性,否则会降低实体化视图中数据的新鲜度,并影响OLAP查询结果的真实性和有效性.为此,本文提出了基于时间戳的动态视图维护技术.该技术采用版本链控制技术,通过时间戳的控制进一步使视图更新和查询的同步进行,有效地解决了由于OLTP更新事务和OLAP事务同时访问数据所发生冲突的问题,在满足视图联机实时维护的同时,更好的提高了数据仓库的新鲜度和OLAP的查询效率.     

数据仓库中基于实体化辅助视图的视图增量维护

为了加快对大量数据的查询处理速度，通常在数据仓库以实视图方式存储数据，当基础数据发生变化时，这些实视图也必须随着更新，因而视图自维护和一致性维护成为数据仓库的重要问题。本文提出利用视图计算的中间结果创建辅助视图，在数据仓库中进行实体化，采用有效的增量维护算法计算实视图的精确变化，实现数据仓库视图自维护。     

数据仓库实化视图的联机维护

数据仓库实化视图的联机维护是数据仓库系统维护的一项关键技术,采用这种技术,能够在不影响用户正常业务的情况下,实现数据仓库中实化视图数据的及时更新。但联机分析处理（OLAP）作为数据仓库的一个主要应用,在数据仓库实化视图的联机维护过程中会产生严重的数据不 一致问题。为了解决这个问题,引入“维护库”（Maintaining Database)的概念,提出基于事务触发的视图维护算法TVM,采取应答机制,达到数据的一致性。     

基于多实化视图增量维护的基库生成算法

数据仓库的维护是数据仓库应用中的一个十分重要的问题,近几年产生了很多的维护算法。已有的维护算法多是针对单个实化视图的维护;或只针对简单SPJ视图的维护;或只针对聚集函数的维护;而实际的数据仓库大多是由包含聚集函数的多个实化视图组成。因此综合考虑包含聚集函数的多个实化视图的维护问题是必然的。文章正是在此情况下提出了一种基于多实化视图增量维护的基库生成算法,在《基于基库的多实化视图增量维护算法》中提出了包含聚集函数的多实化视图的维护算法。     

DM3多数据源数据仓库的一致性维护算法研究

数据仓库是一个集成了多个分布式、自治或异构数据源上的信息的数据储藏室,以支持用户的查询和分析。本文介绍了DM3数据仓库实现我数据源实化视一致性维护的策略,分析了产生视图不一致性的原因和解决办法,以及改进后的一致性维护算法：Strobe*算法和T－Strobe*算法。     

基于Agent的三层数据仓库系统体系结构的研究

针对传统数据仓库体系结构的不足，引入了软件Agent技术以及"数据仓库基库" 的概念，提出了一种基于Agent的三层数据仓库系统体系结构，一方面使得传统数据库更加 容易融入到数据仓库系统，增强了整个系统的伸缩性；另一方面，由于Agent技术的介入， 降低了网络数据传输量，适应了移动环境等复杂信息源的要求，拓广了数据仓库的应用领域 。     

Updating distributed materialized views

The problem of updating materialized views in distributed database systems is discussed. An architecture and detailed procedures for updating a collection of remote views with arbitrary refresh times by using a single differential file are described. The efficiency of the update procedure is enhanced by adopting s multiquery optimization approach and by introducing a powerful prescreening procedure to eliminate differential tuples. It is shown that even for a single remote view, there are many instances where the update procedure performs better (with respect to total I/O and communication costs) than a base table approach     

数据仓库中实体化视图的一致性维护问题的研究

实现实体化视图是提高系统响应时间的一个关键技术和有效的解决方案,但在具体的实现过程中面临着实体化视图的一致性维护问题。由于源数据的不稳定性,其结构和数据的变化必须及时传播到实体化视图中,以保持实体化视图与源数据的变化一致性,否则会降低实体化视图中数据的新鲜度,并影响OLAP查询结果的真实性和有效性。为此本文针对视图的一致性维护问题,介绍了视图中数据的维护方法,同时还就实体化视图结构的维护,提出了使视图重计算代价最小化的解决方案和基本算法。     

面向区块链的高效物化视图维护和可信查询

区块链具有去中心化、不可篡改和可追溯等特性,可应用于金融、物流等诸多行业.由于所有交易数据按照交易时间顺序存储在各个区块,相同类型的交易数据通常会散布在诸多区块之中,降低了面向历史区块的追溯查询的处理效率.索引构建和物化视图是提升查询性能的两种典型方法,但当待处理数据分布于多个区块时,使用索引无法改善I/O访问效率,而物化视图可有效应对这个问题.然而,由于区块链系统的特点明显区别于关系数据库,传统的面向关系数据库的物化视图技术无法被直接应用到区块链之中.鉴于此,首次提出一种面向区块链的高效物化视图机制,具有如下特征:(1)将视图维护操作与共识过程同时执行,降低该操作对系统性能的影响;(2)使用字典树加快以区块为单位的多物化视图维护进程;(3)以默克尔验证的方式确保物化结果不被恶意篡改,进而确保查询结果可信.所提出的物化视图维护机制已经被集成到一个区块链系统中,并通过实验来验证该机制的高效性.     

Incremental View Maintenance for Mobile Databases

In a mobile environment, querying a database at a stationary server from a mobile client is expensive due to the limited bandwidth of a wireless channel and the instability of the wireless network. We address this problem by maintaining a materialized view in a mobile client's local storage. Such a materialized view can be considered as a data warehouse. The materialized view contains results of common queries in which the mobile client is interested. In this paper, we address the view update problem for maintaining the consistency between a materialized view at a mobile client and the database server. The content of a materialized view could become incoherent with that at the database server when the content of the database server and/or when the location of the client is changed. Existing view update mechanisms are ‘push-based’. The server is responsible for notifying all clients whose views might be affected by the changes in database or the mobility of the client. This is not appropriate in a mobile environment due to the frequent wireless channel disconnection. Furthermore, it is not easy for a server to keep track of client movements to update individual client location-dependent views. We propose a ‘pull-based’ approach that allows a materialized view to be updated at a client in an incremental manner, requiring a client to request changes to its view from the server. We demonstrate the feasibility of our approach with experimental results. Received 27 January 1999 / Revised 26 November 1999 / Accepted 17 April 2000     

Updating knowledge bases while maintaining their consistency

When updating a knowledge base, several problems may arise. One of the most important problems is that of integrity constraints satisfaction. The classic approach to this problem has been to develop methods forchecking whether a given update violates an integrity constraint. An alternative approach consists of trying to repair integrity constraints violations by performing additional updates thatmaintain knowledge base consistency. Another major problem in knowledge base updating is that ofview updating, which determines how an update request should be translated into an update of the underlying base facts. We propose a new method for updating knowledge bases while maintaining their consistency. Our method can be used for both integrity constraints maintenance and view updating. It can also be combined with any integrity checking method for view updating and integrity checking. The kind of updates handled by our method are: updates of base facts, view updates, updates of deductive rules, and updates of integrity constraints. Our method is based on events and transition rules, which explicity define the insertions and deletions induced by a knowledge base update. Using these rules, an extension of the SLDNF procedure allows us to obtain all possible minimal ways of updating a knowledge base without violating any integrity constraint.     

Web仓储中的单视图一致性

Web仓储系统使用物化视图的方法管理和维护Web数据，它可以给用户的查询和分析带来更快的效率，特别适合联机分析处理(OLAP)和决策支持。Web环境中数据更新非常频繁，为保持系统的时新性(freshness)，需要不断刷新物化视图，在Web视图刷新的过程中，必须保持物化视图与基础数据之间的一致性(称为单视图一致性，SVC)，否则系统中的数据就会产生错误，进而影响用户的正确使用，围绕这种单视图一致性，针对Web环境的特性，给出了相关保持算法，这些算法具有良好的Web环境适应性和伸缩性。     

View adaptation in the fragment-based approach

View adaptation relies on adapting a set of materialized views in response to schema changes of source relations and/or after view redefinition. Recently, several view selection methods that are based on materializing fragments of the view rather than the whole view have been proposed. We call this approach the fragment-based approach. This paper presents a view adaptation method in the fragment-based approach, which is aimed at exploiting the opportunities to share not only materialized data, but also computation between the different views. In order to do this, the views are modeled using the so-called multiview materialization graph, which represents the views as a bipartite directed acyclic graph whose nodes are operations and fragments of the views. Then, the adaptation is performed regarding all materialized views and not solely the old materialization of the view. However, the data independence is preserved for the views that are not affected by the change. On the contrary, in related work, the adaptation technique is based solely on the old materialization of the same view. We studied the impact of the fragmentation on the adaptation techniques and showed the advantages and drawbacks of this approach.     

Maintaining Views in Object-Relational Databases

View materialization is an important way of improving the performance of query processing. When an update occurs to the source data from which a materialized view is derived, the materialized view has to be updated so that it is consistent with the source data. This update process is called view maintenance. The incremental method of view maintenance, which computes the new view using the old view and the update to the source data, is widely preferred to full view recomputation when the update is small in size. In this paper we investigate how to incrementally maintain views in object-relational (OR) databases. The investigation focuses on maintaining views defined in OR-SQL, a language containing the features of object referencing, inheritance, collection, and aggregate functions including user-defined set aggregate functions. We propose an architecture and algorithms for incremental OR viewmaintenance. We implement all algorithms and analyze the performance of them in comparison with full view recomputation. The analysis shows that the algorithms significantly reduce the cost of updating a vieww hen the size of an update to the source data is relatively small. Received 23 May 2000 / Revised 27 March 2001 / Accepted in revised form 30 April 2001 Correspondence and offprint requests to: Jixue Liu, School of Computer and Information Science, University of South Australia, Mawson Lakes, Adelaide SA5084, Australia. Email: jixue.liu@unisa.edu.auau