SQL Server 2005 OLAP技术研究与实现

简要介绍了OLAP的概念和特点;从OLAP最新概念和技术等方面,解释了微软SQL Server 2005中统一维度模型;研究了微软现代OLAP搭建方法与传统搭建方法的差异;通过基于企业销售数据多维数据集的OLAP实例,说明了微软最新OLAP的设计概念的可行性,有助于企业方便快捷地建立自已的分析决策支持平台。     

一种OLAP应用系统的设计和实现

通过对数据仓库和OLAP概念及体系结构的分析,描述了一种OLAP应用系统的设计方案,并介绍了它的具体实现方法。基于数据仓库的查询,一般都是及时特定查询,要在严格的响应时间内执行复杂的查询,遍历百万上亿的记录,同时进行可能很复杂的搜索、连接和汇总的操作。查询的数据吞吐量和响应时间是判断数据仓库性能的重点。CUBE的计算是OLAP及时查询的基础,提高查询的速度需要对OLAP进行预先的计算。文中系统比较了一些计算立方体的算法,并运用到具体的系统当中。     

基于OLAP技术实现银行客户分析系统

通过构建针对银行客户分析的数据仓库,描述了O鄄LAP系统的设计和实现方法,分析了OLAP服务器的内部结构,并着重研究了OLAP多维立方体的创建和OLAP多维引擎的构造。     

教学评价与知识发现系统的设计

提出了一个基于联机分析技术（OLAP）的教学评价与知识发现,给出了由学生,知识点和类别等构成的六个维度的数据立方体以及利用OLAP技术和关联规则对该数据立方体进行数据挖掘的解决方案。利用上述方法对学生的考试系统进行挖掘,得出有用的结论,从而指导学校的教学工作。     

基于影响空间的多维对象代数结构

介绍了OLAP模型,OLAP数据采掘的体系结构,指出它与决策支持系统之间的关系.描述了多维对象代数结构的基本概念及所涉及的计算.最后讨论了cube与影响域之间的联系与区别.     

基于Analysis Services的教学评估分析系统的设计

在本科教学水平评估的工作实践中,如何对评估指标进行有效跟踪和自评,为教学的科学决策提供依据,同时避免传统事务统计型管理软件的固有缺陷,增强对教学决策的支持,是一个重要课题。提出构建基于Analysis Services的分析型应用系统,系统的基础是数据仓库和OLAP。在给出了主题分析与数据立方体的设计、ETL设计、分析界面设计等技术方案的同时,也阐述了系统实施的相关内容。     

一种应用于OLAP的数据模型及其实现

数据仓库及OLAP技术是当前数据库领域研究的热点,而数据模型又是数据仓库及OLAP核心基础。文章提出了一种应用于OLAP的数据模型,并用于实际应用中。这种数据模型在概念上表达了OLAP特性,支持OLAP操作,而且其数学代数简单明白地表达了OLAP查询。     

MS OLAP数据立方自动增量更新的程序实现

针对MS OLAP对海量数据立方体进行完全更新非常耗时,以及在它的服务器管理器中实现数据立方体的手动增最更新操作繁琐,需要由熟悉MDX语言的专业人员完成的问题,文章提出了基于事实表上时间戳或其它标志的自动增量更新方案,并给出了C#编写的程序示例。     

OLAP技术及其在SQL SERVER 2000中的实现

OLAP系统积累的大量数据如何为决策分析提供支持将成为下一阶段数据库应用的重点，OLAP技术通过数据立方体提供多维度的数据视图，并通过旋转切片等操作扩展查询语言的功能来满足这种需求。文中简要介绍了联机分析处理(On line Analysis Process)中的基本概念、OLAP与传统联机事务处理OLTP的差别、OLAP的相关实现技术，并给出了OLAP在SQL SERVER 2000中的具体实现。最后对OLAP的发展前景作出展望。     

Improving the performance and functionality of Mondrian open‐source OLAP systems

For a long time, the design of relational databases has focused on the optimization of atomic transactions (insert, select, update or delete). Currently, relational databases store tactical information of data warehouses, mainly for select‐like operations. However, the database paradigm has evolved, and nowadays on‐line analytical processing (OLAP) systems handle strategic information for further analysis. These systems enable fast, interactive and consistent information analysis of data warehouses, including shared calculations and allocations. OLAP and data warehouses jointly allow multidimensional data views, turning raw data into knowledge. OLAP allows ‘slice and dice’ navigation and a top‐down perspective of data hierarchies. In this paper, we describe our experience in the migration from a large relational database management system to an OLAP system on top of a relational layer (the data warehouse), and the resulting contributions in open‐source ROLAP optimization. Existing open‐source ROLAP technologies rely on summarized tables with materialized aggregate views to improve system performance (in terms of response time). The design and maintenance of those tables are cumbersome. Instead, we intensively exploit cache memory, where key data reside, yielding low response times. A cold start process brings summarized data from the relational database to cache memory, subsequently reducing the response time. We ensure concurrent access to the summarized data, as well as consistency when the relational database updates data. We also improve the OLAP functionality, by providing new features for automating the creation of calculated members. This makes it possible to define new measures on the fly using virtual dimensions, without re‐designing the multidimensional cube. We have chosen the XML/A de facto standard for service provision. Copyright © 2008 John Wiley & Sons, Ltd.     

iDiff: Informative Summarization of Differences in Multidimensional Aggregates

Multidimensional OLAP products provide an excellent opportunity for integrating mining functionality because of their widespread acceptance as a decision support tool and their existing heavy reliance on manual, user-driven analysis. Most OLAP products are rather simplistic and rely heavily on the user's intuition to manually drive the discovery process. Such ad hoc user-driven exploration gets tedious and error-prone as data dimensionality and size increases. Our goal is to automate these manual discovery processes. In this paper we present an example of such automation through a iDiff operator that in a single step returns summarized reasons for drops or increases observed at an aggregated level.We formulate this as a problem of summarizing the difference between two multidimensional arrays of real numbers. We develop a general framework for such summarization and propose a specific formulation for the case of OLAP aggregates. We develop an information theoretic formulation for expressing the reasons that is compact and easy to interpret. We design an efficient dynamic programming algorithm that requires only one pass of the data and uses a small amount of memory independent of the data size. This allows easy integration with existing OLAP products. Our prototype has been tested on the Microsoft OLAP server, DB2/UDB and Oracle 8i. Experiments using the OLAP benchmark demonstrate (1) scalability of our algorithm as the size and dimensionality of the cube increases and (2) feasibility of getting interactive answers with modest hardware resources.     

OLAP分析模型在集团企业财务分析中的应用

从OLAP分析的代数实现出发，提出了一种OLAP分析模型，包括一个多维立方体数据模型和一系列多维算子模型，并将此OLAP分析模型应用于集团企业的财务分析，提供了各种财务分析方法的代数实现。     

OLAP在银行数据仓库中的设计和实现

如何对海量数据进行科学的分析处理以便为决策者提供决策支持已经成为银行面临的新课题.对此,通过对数据仓库在银行业务应用的研究,围绕银行领域的数据仓库和联机分析处理的部分设计开发过程进行了广泛的论述.主要综述了实体关系模型、星型模型、立方体（CUBE）、汇总表的设计以及在开发中所要注意的问题,并且特别对目前使用比较多的立方体设计进行深入有效的讨论,提出了一种高效的建立数据仓库和实现联机分析处理的设计方案和实现思想.     

OLAP核心技术—数据立方体的研究现状与展望

该文从OLAP的基本功能出发，综述了其核心技术－数据立方体（Data Cube)的研究现状，主要讨论了3个方面：数据立方体建模，数据立方体计算和数据立方体操作，最后展望了该领域的研究方向。     

The automatic creation of OLAP cube using an MDA approach

The Model‐Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation. To deal with this problem, we propose in this paper an MDA solution to automate the process of getting OLAP cube and its implementation through a set of metamodels and automatic transformations among them. In fact, the proposal generates the OLAP and DW relational models (PSMs) from the conceptual one, using also a PDM model that describes the target business intelligence platform. After that, the source code to create the cube is got from both PSM models. For this aim, we define a set of transformation rules implemented using the Atlas transformation language. Finally, a case study will be provided to validate our approach.     

Efficient Incremental Maintenance for Distributive and Non-Distributive Aggregate Functions

Data cube pre-computation is an important concept for supporting OLAP （Online Analytical Processing） and has been studied extensively. It is often not feasible to compute a complete data cube due to the huge storage requirement. Recently proposed quotient cube addressed this issue through a partitioning method that groups cube cells into equivalence partitions. Such an approach not only is useful for distributive aggregate functions such as SUM but also can be applied to the maintenance of holistic aggregate functions like MEDIAN which will require the storage of a set of tuples for each equivalence class. Unfortunately, as changes are made to the data sources, maintaining the quotient cube is non-trivial since the partitioning of the cube cells must also be updated. In this paper, the authors design incremental algorithms to update a quotient cube efficiently for both SUM and MEDIAN aggregate functions. For the aggregate function SUM, concepts are borrowed from the principle of Galois Lattice to develop CPU-efficient algorithms to update a quotient cube. For the aggregate function MEDIAN, the concept of a pseudo class is introduced to further reduce the size of the quotient cube, Coupled with a novel sliding window technique, an efficient algorithm is developed for maintaining a MEDIAN quotient cube that takes up reasonably small storage space. Performance study shows that the proposed algorithms are efficient and scalable over large databases.     

数据仓库中的一种立方体数据模型

数据仓库和联机分析处理(OLAP)是商业数据处理领域中的两个最重大的新技术。OLAP应用要求对数据仓库中存储的大量数据进行分析,用标准关系数据库技术来实现非常复杂的查询是相当困难的。所以,在数据仓库中,数据被组织成立方体数据模型。该文提出了一种简单、直观的数据立方体模型以及在这个立方体上支持OLAP操作的代数。为复杂的查询提供了简要的表述方法。     

基于Analysis Services的教学评估分析系统的设计

在本科教学水平评估的工作实践中,如何对评估指标进行有效跟踪和自评,为教学的科学决策提供依据,同时避免传统事务统计型管理软件的固有缺陷,增强对教学决策的支持,是一个重要课题。提出构建基于Analysis Services的分析型应用系统,系统的基础是数据仓库和OLAP。在给出了主题分析与数据立方体的设计、ETL设计、分析界面设计等技术方案的同时,也阐述了系统实施的相关内容。     

Compressed Data Cube for Approximate OLAP Query Processing

Approximate query processing has emerged as an approach to dealing with the huge data volume and complex queries in the environment of data warehouse.In this paper,we present a novel method that provides approximate answers to OLAP queries.Our method is based on building a compressed (approximate) data cube by a clustering technique and using this compressed data cube to provide answers to queries directly,so it improves the performance of the queries.We also provide the algorithm of the OLAP queries and the confidence intervals of query results.An extensive experimental study with the OLAP council benchmark shows the effectiveness and scalability of our cluster-based approach compared to sampling.     

Online mining of fuzzy multidimensional weighted association rules

This paper addresses the integration of fuzziness with On-Line Analytical Processing (OLAP) based association rules mining. It contributes to the ongoing research on multidimensional online association rules mining by proposing a general architecture that utilizes a fuzzy data cube for knowledge discovery. A data cube is mainly constructed to provide users with the flexibility to view data from different perspectives as some dimensions of the cube contain multiple levels of abstraction. The first step of the process described in this paper involves introducing fuzzy data cube as a remedy to the problem of handling quantitative values of dimensional attributes in a cube. This facilitates the online mining of fuzzy association rules at different levels within the constructed fuzzy data cube. Then, we investigate combining the concepts of weight and multiple-level to mine fuzzy weighted multi-cross-level association rules from the constructed fuzzy data cube. For this purpose, three different methods are introduced for single dimension, multidimensional and hybrid (integrates the other two methods) fuzzy weighted association rules mining. Each of the three methods utilizes a fuzzy data cube constructed to suite the particular method. To the best of our knowledge, this is the first effort in this direction. We compared the proposed approach to an existing approach that does not utilize fuzziness. Experimental results obtained for each of the three methods on a synthetic dataset and on the adult data of the United States census in year 2000 demonstrate the effectiveness and applicability of the proposed fuzzy OLAP based mining approach. OLAP is one of the most popular tools for on-line, fast and effective multidimensional data analysis. In the OLAP framework, data is mainly stored in data hypercubes (simply called cubes).