一种基于维层次编码的OLAP聚集查询算法

联机分析处理(OLAP)查询往往需在海量数据上进行即席的复杂分组聚集查询，在其SQL语句中通常包含多表连接和分组聚集操作，因而减少多表连接和压缩关键字，以及对查询数据进行有效地分组聚集操作，成为ROLAP查询处理的关键问题。提出了一种基于维层次编码的新型预分组聚集算法DHEPGA．DHEPGA算法充分利用了编码长度较小的维层次编码及其前缀，来快速检索出与查询关键字相匹配的维层次编码，求得维层次属性的查询范围，减少了I／O开销，提高了OLAP查询效率。理论分析和实验结果表明，DHEPGA算法性能是非常有效的。     

一种P2P网络环境下的OLAP模式匹配方案

P2P网络环境下,每个OLAP服务器上都有一套完整的数据解决方案。当OLAP服务器之间进行协同工作时,如何将Da-taCube中的模式进行匹配以完成联合OLAP查询成为一个关键问题。在对DataCube中的维及维层次链进行了定义后,提出了一种维层次链的匹配规则,能较好地优化P2P网络环境下执行联合OLAP查询。     

一个面向OLAP应用的多维数据查询语言及其在对象关系数据库中的实现

该文提出了一个面向联机分析处理(OLAP)的多维查询语言,为多维查询的方便表达提供了一个直观的高层界面。这一查询语言的独特之处在于能够很好地支持具有继承关系的维层次结构,这种维层次结构在传统的关系型的ROLAP中是难于用星型/雪花模式表示的。文章将这一查询语言实现于对象关系数据库之上。并通过实例进行说明,对象关系数据库所特有的基本类型扩展能力、复杂对象的表示以及继承机制,使得对多维数据的描述更加灵活、多维操作更为高效。     

支持非覆盖维的扩展多维数据模型

非覆盖维等复杂结构维的处理一直都是数据仓库领域的难题,本文在详细分析非覆盖维特性的基础上,提出了一个扩展的多维数据模型,改变了传统的级别间的映射关系的定义方式,定义了从父级别到子级别分区的映射关系,从而实现了对非覆盖维和非平衡维的支持,并能够完整地表达各种复杂维层次结构语义.同时,基于DAG图描述的维层次结构,定义了基于该多维模型的立方体代数和OLAP操作.将多维数据库概念模型中的维与度量的转换操作引入到OLAP操作集合中,使其支持复杂维的转化操作,进而丰富和增强基于该模型的OLAP系统的分析能力.     

基于有向图描述数据仓库中复杂维层次结构的方法研究

针对数据仓库中复杂维结构的特点，提出建立基于有向图的维字典图以辅助联机分析中有关维层次的定义，并利用维字典图重新定义了数据仓库多维数据模型和关于维层次的重要OLAP操作．该方法能够在复杂维层次结构的情况下灵活地适应用户的分析需求，提高了联机分析系统的处理能力．     

SQL Server增OLAP

Microsoft SQL Server OLAP Services的最终特性和附加的独立软件开发商(ISV)支持,于本月公之于众。这些特性将使Microsoft SQL Server 7.0在今年晚些时候发行时成为可提供企业级OLAP解决方案的平台。定于1998年第四季度发售的Microsoft SQL Server 7.0将提供包括回写、成员属性、分布式查询解析等特性。微软已在Microsoft SQL Server OLAP Services中实现的回写具有事务型语义和多用户并发功能。这种特性使用了Microsoft SQL Server OLAP Service中的分层架构和分组,并使用关系数据库来管理事务。回写除为独立软件开发商和客户应用程序提供假设分析以外,还提供了进行财务预算和预报的功能;成员属性则使用户能够查询存储于多维OLAP立方体中的额外信息。这些属性可同常规一样用来对OLAP数据进行     

语义Cube的层次聚类方法

在DataCube语义特性及模式中维的层次性基础上提出了多维数据之间的等价关系≡HCov,对DataCube进行层次聚类·该方法的优点在于用等价类的方法保存了所有聚集记录,同时定义了等价类之间的关系,以保存聚集记录之间的层次信息·理论分析和实验证明,该方法不但节省了存储空间,而且利用聚类信息及层次信息,可高效的进行各种OLAP查询,以及支持多维数据上的上钻下钻、旋转等Cube语义操作·同时,为OLAP查询导航、OLAP查询行为分析的实现提供了可能·     

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。     

大型数据库对象关系技术的应用研究

对象关系数据库支持SQL语言，对复杂的SQL3查询进行查询优化以支持复杂查询，也支持用户定义自己的抽象数据类型，以及由基类组合成复杂的组合类型的能力，从而支持了复杂的数据，论述了对象关系数据库在RDBMS上进行扩展通常的四个方面，研究与讨论对象关系数据库的支持基类扩充技术和支持复杂对象技术，最后基于Oracle8i/9i对象关系数据库进行分析研究和示例操作。     

复杂对象的即席查询语言QBHF

本文介绍了支持扩充的ＮＦ＾２模型ＨＲＤＭ的即席查询语言ＱＢＨＦ。用户可方便地使用ＱＢ－ＨＦ定义具有层次结构的复杂对象以及对象之间的联系，构造复杂的查询，对数据库进行编辑、更新等操作。     

面向空间数据库引擎的扩充数据模型及其操纵语言GSQL

面向空间数据库引擎的扩充数据模型将点、线、面等几何数据类型作为系统的基本数据类型,其操纵语言GSQL在标准SQL语言基础上支持几何数据和属性数据的定义、检索、存储和管理。关系数据库元数据表提供了对象—关系模型转换接口。     

A foundation for capturing and querying complex multidimensional data

On-line analytical processing (OLAP) systems considerably improve data analysis and are finding wide-spread use. OLAP systems typically employ multidimensional data models to structure their data. This paper identifies 11 modeling requirements for multidimensional data models. These requirements are derived from an assessment of complex data found in real-world applications. A survey of 14 multidimensional data models reveals shortcomings in meeting some of the requirements. Existing models do not support many-to-many relationships between facts and dimensions, lack built-in mechanisms for handling change and time, lack support for imprecision, and are generally unable to insert data with varying granularities. This paper defines an extended multidimensional data model and algebraic query language that address all 11 requirements. The model reuses the common multidimensional concepts of dimension hierarchies and granularities to capture imprecise data. For queries that cannot be answered precisely due to the imprecise data, techniques are proposed that take into account the imprecision in the grouping of the data, in the subsequent aggregate computation, and in the presentation of the imprecise result to the user. In addition, alternative queries unaffected by imprecision are offered. The data model and query evaluation techniques discussed in this paper can be implemented using relational database technology. The approach is also capable of exploiting multidimensional query processing techniques like pre-aggregation. This yields a practical solution with low computational overhead.     

Improving performance by creating a native join-index for OLAP

The performance of online analytical processing (OLAP) is critical for meeting the increasing requirements of massive volume analytical applications. Typical techniques, such as in-memory processing, column-storage, and join indexes focus on high performance storage media, efficient storage models, and reduced query processing. While they effectively perform OLAP applications, there is a vital limitation: mainmemory database based OLAP (MMOLAP) cannot provide high performance for a large size data set. In this paper, we propose a novel memory dimension table model, in which the primary keys of the dimension table can be directly mapped to dimensional tuple addresses. To achieve higher performance of dimensional tuple access, we optimize our storage model for dimension tables based on OLAP query workload features. We present directly dimensional tuple accessing (DDTA) based join (DDTAJOIN), a technique to optimize query processing on the memory dimension table by direct dimensional tuple access. We also contribute by proposing an optimization of the predicate tree to shorten predicate operation length by pruning useless predicate processing. Our experimental results show that the DDTA-JOIN algorithm is superior to both simulated row-store main memory query processing and the open-source column-store main memory database MonetDB, thanks to the reduced join cost and simple yet efficient query processing.     

Extensions to SQL for historical databases

A historical management system (HDBMS) is described which uses an extended relational data model with state-oriented, instead of `cubic', conceptualization. Two types of historical relations, called state and event relations, are provided for modeling real-world objects. The query language SQL has been extended for definition, retrieval, and update of historical relations. The extended SQL, called HSQL, is a superset of SQL. The author defines a few primitive algebra operations for historical relations, and uses them as a basis for extensions to SQL. In this way, HSQL retains the elegant structural and algebraic framework of SQL. HSQL contains a few new clauses, many operations and built-in functions on time domain, and facilities for retrospective updates and time-rollback     

Dimensional enrichment of statistical linked open data

On-Line Analytical Processing (OLAP) is a data analysis technique typically used for local and well-prepared data. However, initiatives like Open Data and Open Government bring new and publicly available data on the web that are to be analyzed in the same way. The use of semantic web technologies for this context is especially encouraged by the Linked Data initiative. There is already a considerable amount of statistical linked open data sets published using the RDF Data Cube Vocabulary (QB) which is designed for these purposes. However, QB lacks some essential schema constructs (e.g., dimension levels) to support OLAP. Thus, the QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP. In this paper, we focus on the enrichment of an existing QB data set with QB4OLAP semantics. We first thoroughly compare the two vocabularies and outline the benefits of QB4OLAP. Then, we propose a series of steps to automate the enrichment of QB data sets with specific QB4OLAP semantics; being the most important, the definition of aggregate functions and the detection of new concepts in the dimension hierarchy construction. The proposed steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion. The user can enrich the QB data set with QB4OLAP concepts (e.g., full-fledged dimension hierarchies) by choosing among the candidate concepts automatically discovered with the steps proposed. Finally, we conduct experiments with 25 users and use three real-world QB data sets to evaluate our approach. The evaluation demonstrates the feasibility of our approach and shows that, in practice, our tool facilitates, speeds up, and guarantees the correct results of the enrichment process.     

多维数据模型中维层次结构的自动生成算法及其实现

多维数据模型是数据仓库及联机分析处理的核心,目前主要有两种:星型模型和雪花模型。维层次结构是多维数据模型最重要的概念之一。该文提出了一个算法,它能够在雪花模型中根据维表间的依赖关系构造维的层次结构。指出使用该算法的前提并进行了证明,随后实现了算法。文章最后提出了在星型模型中构造维的层次结构的基本过程。     

一种改进的分组序号聚集算法

ROLAP是OLAP（联机分析处理）中使用最广泛的一种类型,其主要功能是管理决策所需要的总结数据。总结数据一般都涉及多表连接和分组聚集操作,提高这些操作的性能成为提高OLAP操作响应速度的关键。为此,提出一种基于分组序号的新聚集算法IMuGA。算法充分利用时间维表特殊性,通过对事实表关键字直接获得分组属性值,减少了多表连接中时间维度的连接次数,提高了联机分析处理查询效率。实验结果表明,该算法是有效的。     

时态数据仓库的设计与实现

数据仓库多维模式的变化会影响OLAP查询结果的正确性,因此有必要对多维模式的历史变化进行维护。文章给出了时态数据仓库多维模型的一种形式定义,并在此定义的基础上,设计并实现了具有时态特性的煤矿企业产量数据仓库。