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。     

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.     

基于P2P的实化视图维护技术研究

基于P2P的视图维护是一个重要的研究方向。当前的视图维护方法主要以传统的C／S模型为基础，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，构建了基于P2P的主动视图维护模型，模型充分发掘P2P技术的潜在优势，克服了传统视图维护方法中数据仓库和数据源负载失衡及可伸缩瓶颈等问题。描述了P2PAVM的节点拓扑结构；重点阐述了P2PAVM的动态节点管理与路由等关键技术。     

基于模式更新的实视图一致性维护策略

通过定义不同数据源更新之间的并发依赖关系和同源依赖关系,利用概念化事务模型可实现松散耦合环境中视图的并发维护。在此基础上,提出VMSCNF算法,解决不稳定网络环境中基于模式更新的实视图一致性维护问题,并通过实验原型验证了算法有效性。     

实现物化视图在线维护

数据仓库在线条件下,物化视图的维护是一项非常具有挑战性的工作.从减少不必要的更新和选择适当的更新时机入手进行较为深入的研究,提出机会更新的维护方法OUMV,并将它与延迟更新进行结合,而获得一种更为有效的在线维护算法ODUA,以克服延迟更新算法存在的不足,实现在线条件下物化视图的高效维护.     

分布式数据源的实视图维护算法研究

数据仓库作为决策支持系统的集成化数据中心,其数据可以认为是定义在多个不同数据源的实视图集。近年来数据仓库中实视图维护算法的研究激起很多学者的重视。当多个独立的数据源出现并发更新时传统的实视图维护算法可能导致视图维护异常,本文提出了一个双向扫描并行处理实视图维护(BSP)算法,能确保实视图与数据源的完全一致性,并通过实验与其它类似的算法进行了比较,说明本算法具有较高的效率。     

基于更新补偿的实化视图增量过滤研究

针对WHIPS实化视图维护模型不能检测无效增量问题,提出了增量过滤机制.该机制利用视图定义中的约束条件,并通过更新补偿机制,对基表增量进行有效性检测和过滤,减少了网络通信并避免了数据仓库端的无效的视图增量计算.文章最后通过实验对比,证明了这种机制的有效性.     

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。     

数据仓库中物化视图维护算法的分析和比较

随着数据源的更新,数据仓库中的物化视图必须得到及时的更新维护.而如何对物化视图进行高效的更新,以满足用户对查询响应速度和查询结果一致性、时新性的要求,这是数据仓库技术中非常复杂和重要的工作,也是一个迫切需要解决的关键性技术问题.以物化视图更新维护问题为主要研究对象,通过对现有各种维护算法深入的研究和分析,系统地进行了比较和总结,最后指出了谊问题深入研究的方向.     

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.     

某于数据源自动侦测更新的并行视图维护模型

数据仓库体系结构是数据仓库建设和维护的重要理论基石，传统的体系框架简单易行，但不够完善。斯坦福大学提出的WHIPS模型解决了信息源自动侦测更新的问题，但由于模型自身存在的瓶颈，使并行更新处理产生阻塞现象。为此，本文提出了一个改进方案，引入了时间戳单元，增加了其中两个重要模块的并行处理能力，并给出一个修正的数
据仓库系统结构。     

Updating Recursive XML Views of Relations

This paper investigates the view update problem for XML views published from relational data.We consider XML views defined in terms of mappings directed by possibly reeursive DTDs compressed into DAGs and stored in relations. We provide new techniques to efficiently support XML view updates specified in terms of XPath expressions with recursion and complex filters.The interaction between XPath recursion and DAG compression of XML views makes the analysis of the XML view update problem rather intriguing.Furthermore,many issues are still open even for relational view updates, and need to be explored.In response to these,on the XML side,we revise the notion of side effects and update semantics based on the semantics of XML views,and present efficient algorithms to translate XML updates to relational view updates. On the relational side,we propose a mild condition on SPJ views,and show that under this condition the analysis of deletions on relational views becomes PTIME while the insertion analysis is NP-complete.We develop an efficient algorithm to process relational view deletions,and a heuristic algorithm to handle view insertions.Finally,we present an experimental study to verify the effectiveness of our techniques.     

基于Hibernate的实化视图维护的研究

在传统的实化视图维护时,数据源把增量数据以XML文档的方式报送给数据仓库,数据仓库从此文档中解析出数据,利用JDBC完成对实化视图的更新。文中提出在数据源把增量数据封装成序列化对象存储于文件中再报送给数据仓库,而数据仓库从文件中读出对象,利用Hibernate直接把对象更新到实化视图。通过两种方案性能的比较,表明后一种方案是可行并且更加高效的。     

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.     

基于分类搜索的SDN流表无环一致性更新方案

在软件定义网络(Soft ware-Defined Networking,SDN)中,由于配置策略的改变导致控制器需要对多个交换机中的流表项进行更新时,会出现更新不一致的情况.其内在原因是控制器无法同时对所有交换机完成更新,不同的更新时延会导致网络状态在逻辑上的不一致,从而影响数据报文的正确转发.针对分类时序更新方案应用场景适用性差和更新时延长,最优化更新方案计算复杂度高等问题,本文在两者的基础上,提出基于分类搜索的无环更新一致性方案(Categorical Search based loop-free Consistent Update scheme,CSCU).方案通过设计交换机分类模型,并在分类的基础上,结合节点依赖思想设计环路搜索优化模型,实现更新时延短,更新效率高的一致性更新.仿真结果表明,本方案有更好的场景适用性和更低的节点操作复杂度,也有更少的更新轮次和更低的计算复杂度,可有效提升更新性能.     

视图维护的并行在线错误纠正算法

最近几年,数据仓维护问题的重心已转移到各信息源并发更新下的视图维护。比较流行的算法,如ECA算法和Strobe算法在解决并发更新问题时需要数据仓处于静止状态。而文中的在线错误纠正方法不需要额外的本地补偿操作,并且在维护时不需要数据仓处于静止状态。进而该文提出了一个在线错误纠正的优化算法,称为并行在线错误纠正算法。该算法对原有的函数模块做了一定的修正和增强处理,并给出了修改了的函数功能模块图,它能完成并行维护,提高维护的性能。     

Independent updates and incremental agreement in replicated databases

Update propagation and transaction atomicity are major obstacles to the development of replicated databases. Many practical applications, such as automated teller machine networks, flight reservation, and part inventory control, do not require these properties. In this paper we present an approach for incrementally updating a distributed, replicated database without requiring multi-site atomic commit protocols. We prove that the mechanism is correct, as it asymptotically performs all the updates on all the copies. Our approach has two important characteristics: it is progressive, and non-blocking.Progressive means that the transaction's coordinator always commits, possibly together with a group of other sites. The update is later propagated asynchronously to the remaining sites.Non-blocking means that each site can take unilateral decisions at each step of the algorithm. Sites which cannot commit updates are brought to the same final state by means of areconciliation mechanism. This mechanism uses the history logs, which are stored locally at each site, to bring sites to agreement. It requires a small auxiliary data structure, called reception vector, to keep track of the time unto which the other sites are guaranteed to be up-to-date. Several optimizations to the basic mechanism are also discussed. Recommended by: Ahmed Elmagarmid