数据仓库实体化视图一致性维护与下查研究

实体化视图作为数据仓库中存储的主要信息实体是由对上一级或外部数据源进行抽取、转化、传输和上载的数据构成的.当源数据发生变化时,如何进行数据仓库实体化视图的一致性维护以及0LAP查询,是一个有着实际意义的研究课题.本文提出一个改进性算法Glide*,该算法采用补偿思想来协调源数据库及实体化视图的一致性,从而对系统内存开销及维护工作量方面都有很大的改进.文章还通过一个示例说明了该算法在实际中的具体运用.     

数据仓库联机维护中一致性问题的研究

数据仓库是存储供查询和决策分析用的集成化信息仓库,它的信息来源于不同地点的数据库或其他信息源.实体化视图是数据仓库中存储的主要信息实体,当原始数据发生变化时,数据仓库中的实体化视图也必须作相应的更新维护.在数据仓库实体化视图的联机维护过程中,由于联机分析处理（On-line Analytical Process,简称OLAP）查询的介入,会产生数据不一致的问题.文章提出了一种MVCA（multiversion compensating algorithm）算法来解决这一问题.MVCA采用版本控制方法,利用补偿思想和应答机制协调数据库和数据仓库之间的更新维护操作,达到保证数据一致的目的.最后,文章通过一个典型示例说明了该算法在实际中的具体应用.     

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。     

数据仓库中多视图环境下的联机维护

数据仓库的视图联机维护是指数数据仓库中的实体化视图实时地与信息源中的数据库仑保持一致,同时不影响前端用户对数据仓库的正常使用。为了解决多视图环境中视图联机维护与下钻查询的一致性问题,文中在数据仓库体系结构中引入了“基库”模型,并提出了相应的视图维护算法３ＶＰＡ。     

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.     

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.     

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。     

数据仓库的集合刷新维护算法

本文提出了一种版本控制集合刷新算法ＶＳＲＡ,它采用增量维护和批处理思想,首先根据原始数据的变化集合,计算出实体化视图的刷新集合,然后据此集合实体化视图,同时利用版本控制保持数据仓库与当前数据库状态的一致性,达到对数据仓库联机维护的目的。ＶＳＲＡ不但减少了数据仓库与数据库之间的通信开销,而且提高了实体化视
视图的刷新效率。严格的版本控制使用户可以随时使用数据仓库进行联机分析处理（ＯＬＡＰ）,并得到正确一     

实体化视图动态选择预处理算法的研究

实体化视图是数据仓库中提高查询效率的有效手段，数据仓库运行期间，需要对其中的实体化视图进行维护，从而保证用户查询的响应时间较短。针对用于实体化视图动态选择的遗传算法收敛速度慢，运行时间长的问题，提出一种预处理算法来计算动态选择实体化视图时遗传算法的初始群体。理论分析和宴验结果表明，该算法可以有效地提高实体化视图动态选择时的寻优收敛速度。     

Consistency Algorithms for Multi-Source Warehouse View Maintenance

A warehouse is a data repository containing integrated information for efficient querying and analysis. Maintaining the consistency of warehouse data is challenging, especially if the data sources are autonomous and views of the data at the warehouse span multiple sources. Transactions containing multiple updates at one or more sources, e.g., batch updates, complicate the consistency problem. In this paper we identify and discuss three fundamental transaction processing scenarios for data warehousing. We define four levels of consistency for warehouse data and present a new family of algorithms, the Strobe family, that maintain consistency as the warehouse is updated, under the various warehousing scenarios. All of the algorithms are incremental and can handle a continuous and overlapping stream of updates from the sources. Our implementation shows that the algorithms are practical and realistic choices for a wide variety of update scenarios.     

数据仓库中实现化视图的一致性维护虎法—ECA算法的实现

数据仓库系统中的视图不仅仅是一个逻辑上的概念,同时也是物理存在的,当数据源上的内容发生变化时,我们必须相应地修改数据仓库中的数据,以保证二者数据的一致性,本文详细介绍了单数原实化视图的一致性维护算法－ECA算法的实现方法。     

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。     

数据仓库多视图的并发控制分析

对传统的２ＰＬ协议冲突操作概念加以扩展,本文提出用依赖图方法判断数据仓库实例化视图调度是可串行化．数据仓库的不同实例化视图访问同一数据源时,这些视图之间的一致性可能得不到保证．本文设计了多视图一致性算法－画笔算法．该算法具有简单性和可扩展性．     

Multiagent immediate incremental view maintenance for data warehouses

Data warehouse systems typically designate downtime for view maintenance, ranging from tens of minutes to hours depending on the system size. We develop a multiagent system that achieves immediate incremental view maintenance (IIVM) for continuous updating of data warehouse views. We describe an IIVM system that processes updates as transactions are executed at the underlying data sources to eliminate view maintenance downtime for the data warehouse-a crucial requirement for internet applications. The use of a multiagent framework provides considerable process speed improvement when compared with other IIVM systems. Since agents are used to delegate the data sources and warehouse views, it is easy to reorganize the components of the system. Through the use of cooperative agents, the data consistency of IIVM can be easily maintained. The test results from this research show that the proposed system increases the availability of the data warehouse while preserving a stringent requirement on data consistency.     

Parallel multisource view maintenance

In a distributed environment, materialized views are used to integrate data from different information sources and then store them in some centralized location. In order to maintain such materialized views, maintenance queries need to be sent to information sources by the data warehouse management system. Due to the independence of the information sources and the data warehouse, concurrency issues are raised between the maintenance queries and the local update transactions at each information source. Recent solutions such as ECA and Strobe tackle such concurrent maintenance, however with the requirement of quiescence of the information sources. SWEEP and POSSE overcome this limitation by decomposing the global maintenance query into smaller subqueries to be sent to every information source and then performing conflict correction locally at the data warehouse. Note that all these previous approaches handle the data updates one at a time. Hence either some of the information sources or the data warehouse is likely to be idle during most of the maintenance process. In this paper, we propose that a set of updates should be maintained in parallel by several concurrent maintenance processes so that both the information sources as well as the warehouse would be utilized more fully throughout the maintenance process. This parallelism should then improve the overall maintenance performance. For this we have developed a parallel view maintenance algorithm, called PVM, that substantially improves upon the performance of previous maintenance approaches by handling a set of data updates at the same time. The parallel handling of a set of updates is orthogonal to the particular maintenance algorithm applied to the handling of each individual update. In order to perform parallel view maintenance, we have identified two critical issues that must be overcome: (1) detecting maintenance-concurrent data updates in a parallel mode and (2) correcting the problem that the data warehouse commit order may not correspond to the data warehouse update processing order due to parallel maintenance handling. In this work, we provide solutions to both issues. For the former, we insert a middle-layer timestamp assignment module for detecting maintenance-concurrent data updates without requiring any global clock synchronization. For the latter, we introduce the negative counter concept to solve the problem of variant orders of committing effects of data updates to the data warehouse. We provide a proof of the correctness of PVM that guarantees that our strategy indeed generates the correct final data warehouse state. We have implemented both SWEEP and PVM in our EVE data warehousing system. Our performance study demonstrates that a manyfold performance improvement is achieved by PVM over SWEEP.Received: 12 November 2001, Accepted: 18 December 2002, Published online: 31 July 2003This work was supported in part by the NSF NYI grant IIS-979624 and NSF CISE Instrumentation grant IRIS 97-29878 and NSF grant IIS-9988776.