一种物化视图维护算法

数据仓库的刷新过程常被看作在数据源上对物化视图的维护问题，但现有的许多物化视图维护算法在不稳定网络中应用会导致数据异常问题，本文基于传统的数据仓库模型，提出了一种新的物化视图维护算法，来完成对数据仓库的有效刷新，并通过验证说明该算法的可行性。     

基于数据集成的多版本控制算法

介绍了一种数据仓库中基于数据集成的多版本控制算法。首先简单介绍了数据集成的概念和数据一致性程度的概念,然后介绍了数据仓库中基于数据集成的多版本算法中的视图维护算法,最后重点介绍了数据仓库中基于数据集成的多版本控制算法中事务和会话如何进行、关系表如何修改、对用户算法描述以及对事务维护算法的描述。     

在数据仓库中如何有效地实现数据立方体的计算

有效的实现数据立方体的计算是提高数据仓库查询效率的有力方法。在设计方体时要对方体维护成本和查询响应时间这两个因素加以考虑，从而产生了方体的部分物化和全物化两种方法。通过对数据仓库中如何有效的进行数据立方体计算的讨论，提出了实现数据立方体部分物化和全物化的算法。     

基于物化视图的查询重写系统设计与实现

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。结合数据库发展的需要,研究了在关系数据库中利用物化视图和查询重写技术存储和查询XML文档。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。通过对物化视图、视图缓存、查询重写等技术进行研究,讨论如何利用关系数据库中缓存的物化视图来回答查询的问题,并实现了基于物化视图的XML数据查询系统原型。系统中使用扩展的模式匹配算法实现对多分支路径的模式匹配,解决了重写是否存在的问题,并通过模式匹配算法的匹配结果,来判断是否可以构造补偿表达式。通过构造补偿表达式算法得到重写结果,进而通过数据库中缓存的视图完成查询。     

数据仓库中物化视图的遗传选择算法

针对贪心算法没有考虑物化视图的更新代价和计算量大等缺点,提出了一个物化视图的遗传选择算法。首先通过候选视图选择算法产生候选视图集;其次提出了优化的代价模型,不仅考虑了查询代价,而且考虑了更新代价;最后从候选视图集中选择出物化视图。该算法与贪心算法相比,降低了计算代价。     

关于实视图维护问题的研究

随着数据仓库技术的迅速发展,实视图作为其中一项可以提供数据存储方式和提高查询响应性能的关键技术得到了充分的重视。但是如何对实视图集进行及时更新,以充分发挥其加快决策查询速度的作用,并满足用户对数据一致性和时新性的要求,却是一个迫切需要解决的关键性技术问题。本文在对实视图维护问题进行了深入研究的基础上,就目前的研究现状作详细的分析和总结。     

数据仓库技术的研究现状和未来方向

数据仓库(Data Warehouse)和联机分析处理(On-Line Analytical Processing,OLAP)是信息技术新兴的研究领城.国际上对数据仓库体系结构、数据组织、视图维护、多维数据库建模、数据立方体计算等问题进行了广泛和深入的研究.阐述了斯坦福大学、IBM Almaden研究中心、威斯康辛大学以及微软和AT&T公司在该领域近几年的研究,以此描述数据仓库技术的研究状况和发展方向.     

七号信令监测系统中基于Oracle的数据同步方案优化

为七号信令监测系统采集的大量数据信息在多个分布式站点同步提供了一个优化方案.该方案基于Oracle的高级复制,利用多级物化视图实现,在保证数据一致性、完整性的前提下实现了上下级数据的快速刷新.     

七号信令监测系统中基于Oracle的数据同步方案研究

七号信令监测系统中采集的大量数据信息需要在多个分布式站点进行同步,本文为其数据的同步提供了一个实现方案。该方案基于Oracle的高级复制,利用多级物化视图实现,在保证数据一致性、完整性的前提下实现了上下级数据的快速刷新。     

用物化视图实现广播监测数据的同步

本文首先介绍了Oracle数据库中物化视图的基本概念,然后举例说明如何利用物化视图在不同数据库之间实现数据同步,最后阐述了物化视图的优点和缺点.     

An evolutionary approach to materialized views selection in a datawarehouse environment

A data warehouse (DW) contains multiple views accessed by queries. One of the most important decisions in designing a DW is selecting views to materialize for the purpose of efficiently supporting decision making. The search space for possible materialized views is exponentially large. Therefore heuristics have been used to search for a near optimal solution. In this paper, we explore the use of an evolutionary algorithm for materialized view selection based on multiple global processing plans for queries. We apply a hybrid evolutionary algorithm to solve three related problems. The first is to optimize queries. The second is to choose the best global processing plan from multiple global processing plans. The third is to select materialized views from a given global processing plan. Our experiment shows that the hybrid evolutionary algorithm delivers better performance than either the evolutionary algorithm or heuristics used alone in terms of the minimal query and maintenance cost and the evaluation cost to obtain the minimal cost     

Materialized view selection as constrained evolutionary optimization

One of the important issues in data warehouse development is the selection of a set of views to materialize in order to accelerate a large number of on-line analytical processing (OLAP) queries. The maintenance-cost view-selection problem is to select a set of materialized views under certain resource constraints for the purpose of minimizing the total query processing cost. However, the search space for possible materialized views may be exponentially large. A heuristic algorithm often has to be used to find a near optimal solution. In this paper, for the maintenance-cost view-selection problem, we propose a new constrained evolutionary algorithm. Constraints are incorporated into the algorithm through a stochastic ranking procedure. No penalty functions are used. Our experimental results show that the constraint handling technique, i.e., stochastic ranking, can deal with constraints effectively. Our algorithm is able to find a near-optimal feasible solution and scales with the problem size well.     

A/sup */ search: an efficient and flexible approach to materialized view selection

Decision support systems issue a large number of online analytical processing (OLAP) queries to access very large databases. A data warehouse needs to precompute or materialize some of such OLAP queries in order to improve the system throughput, since many coming queries can benefit greatly from these materialized views. Materialized view selection with resource constraint is one of the most important issues in the management of data warehouses. It addresses how to fully utilize the limited resource, disk space, or maintenance time to minimize the total query processing cost. This paper revisits the problem of materialized view selection under a disk-space constraint S. Many efficient greedy algorithms have been developed to address this problem. The quality of greedy solutions is guaranteed by a lower bound. However, it is observed that, when S is small, this lower bound can be very small and even be negative. In such cases, their solution quality will not be guaranteed well. In order to improve further the solution quality in such cases, a new competitive A/sup */ algorithm is proposed. It is shown that it is just the distinctive topological structure of the dependent lattice that makes the A/sup */ search a very competitive strategy for this problem. Both theoretical and experimental results show that the proposed algorithm is a powerful, efficient, and flexible approach to this problem.     

TATS: an Efficient Technique for Computing Temporal Aggregates for Data Warehousing

An important use of data warehousing is to provide temporal views over the history of source data. It is significant that nearly all data warehouses are dependent on relational database technology, yet relational databases provide little or no real support for temporal data. Therefore, it is difficult to obtain accurate information for time‐varying data. In this paper, we are going to design a temporal data warehouse to support time‐varying data efficiently. For this purpose, we present a method to support temporal query by combining a temporal query process layer with the relational database which is used as a source database in an existing data warehouse. We introduce the Temporal Aggregate Tree Strategy (TATS), and suggest its algorithm for the way to aggregate the time‐varying data that is changed by the time when the temporal view is created. In addition, The TATS and the materialized view creation method of the existing data warehouse have been evaluated. As a result, the TATS reduces the size of the fact table and it shows a good performance for the comparison factor in case of processing the query for time‐varying data.     

Scheduling the allocation of data fragments in a distributeddatabase environment: a machine learning approach

Different database fragmentation and allocation strategies have been proposed to partially replicate data in a partitioned, distributed database (DDB) environment. The replication strategies include database snapshots, materialized views, and quasi-copies. These strategies are `static' and do not adapt to the changes in the data usage patterns. Furthermore, they often require expensive update synchronizations to maintain data consistency and do not exploit the knowledge embedded in the query history. This paper describes a machine learning based time invariant fragmentation method (MLTIF) that acquires knowledge about the data usage patterns for each node. Based on this knowledge, MLTIF designs time invariant fragments and schedules its allocation and selective update for a specified time period. Simulation is used to compare the effectiveness of the MLTIF approach with that of full replication, materialized views, and nonreplication strategies. Initial results indicate that for most normal operating conditions, the MLTIF approach can be effective     

点击流数据仓库中基于事件驱动的星型ER模型

Web应用产生了大量的点击流数据,为有效分析这些数据,使之发挥更好的决策支持作用,产生了点击流数据仓库。较之传统数据仓库,点击流数据仓库有更复杂的体系结构及更丰富的数据源,现有的数据模型已不再适合点击流数据仓库的发展需要。提出了一种基于事件驱动的星型ER模型作为点击流数据仓库的概念模型,给出了关键事件/非关键事件及主动关注期望/被动关注期望概念。分析结果表明,该模型克服了现有模型的一些不足,能够很好地适应点击流数据仓库的体系结构。     

DataFoundry: information management for scientific data

Data warehouses and data marts have been successfully applied to a multitude of commercial business applications. They have proven to be invaluable tools by integrating information from distributed, heterogeneous sources and summarizing this data for use throughout the enterprise. Although the need for information dissemination is as vital in science as in business, working warehouses in this community are scarce because traditional warehousing techniques do not transfer to scientific environments. There are two primary reasons for this difficulty. First, schema integration is more difficult for scientific databases than for business sources because of the complexity of the concepts and the associated relationships. Second, scientific data sources have highly dynamic data representations (schemata). When a data source participating in a warehouse changes its schema, both the mediator transferring data to the warehouse and the warehouse itself need to be updated to reflect these modifications. The cost of repeatedly performing these updates in a traditional warehouse, as is required in a dynamic environment, is prohibitive. The paper discusses these issues within the context of the DataFoundry project, an ongoing research effort at Lawrence Livermore National Laboratory. DataFoundry utilizes a unique integration strategy to identify corresponding instances while maintaining differences between data from different sources, and a novel architecture and an extensive meta-data infrastructure, which reduce the cost of maintaining a warehouse