OLAP性能测试方法研究与实现

随着商业智能市场的逐步扩大,联机分析处理(OLAP)系统的使用质量评估已经成为数据库应用的研究热点.作为效用特性的OLAP系统性能评估需要一个性能基准.以OLAP委员会推出的APB-1性能基准为基础,首先设计了面向多维数据库的立方体(Cube)模型以及相应的多维表达式(MDX)查询模板,在Cube模型设计的过程中修改了APB-1基准ROLAP星型模型的不足之处;接着在测试数据一致和测试参数一致的前提下,通过对设计的MOLAP模型查询结果与ROLAP模型查询结果进行对比分析,证明了MOLAP模型及MDX查询模板设计的正确性;然后给出了OLAP性能测试流程,描述了支持ROLAP和MOLAP性能测试的工具框架及其主要模块.最后使用该测试框架在商业数据库管理系统上对ROLAP和MOLAP进行并发查询实践,验证了框架的有效性.提出的方法及技术实现为未来OLAP产品性能的测试和评价提供多维数据模型、业务模型和工具的支持.     

MDX编译器设计与实现

讨论了针对OLAP分析服务器中多维查询语言MDX解析模块的设计与实现。研究了基于Lex和Yacc,将MDX查询语句解析成查询树对象的设计思路与实现方法。结合元数据访问和权限管理,编译器从词法分析、语法分析和语义处理等方面进行深入分析,不仅完成了语法和语义的检查,还可基于角色对用户分析查询进行权限验证,实现信息的安全管理。     

采用OLAP技术的在线监测监控系统智能报表的研究(Ⅱ)--系统设计、源代码组成和查询命令

OLAP的功能是面向数据的查询、分析、归纳和总结,用于决策支持和知识发现。本研究组开发的智能报表系统实现了类似OLAP的数据分析和查询功能,总称为SAP系统。SAP采用C／S模式。智能报表系统允许用户按自身需求定制报表,按报表要求对数据进行查询和分析,提供数据的格式化输出和多风格的打印。系统支持MDX(多维表达式)查询,对多个维度的数据的访问更为简单和直观。     

基于分析服务器的智能OLAP系统的实现

实现一个基于MDX查询技术的智能OLAP分析系统SOLAP,利用Analysis Services建立的多维数据集。对于每个多维数据集,根据用户对维的设置,自动产生相应的MDX查询,交给服务器后．利用ADOMD组件获得查询的结果集。在这基础上实现基本OLAP的上卷、下钻、切片、旋转功能,加入智能操作符,并用图形显示。     

采用OLAP技术的在线监测监控系统智能报表的研究(Ⅰ)--系统架构和用户用例分析

OLAP(On-Line Analysis Processing)的功能是面向数据的查询、分析、归纳和总结,用于决策支持和知识发现。本研究组开发的智能报表系统实现了类似OLAP的数据分析和查询功能,总称为SAP系统。SAP采用C／S模式。智能报表系统允许用户按自身需求定制报表,按报表要求对数据进行查询和分析,提供数据的格式化输出和多风格的打印。系统支持MDX(多维表达式)查询,对多个维度的数据的访问更为简单和直观。     

MD-SQL：一种基于MDX的多维数据查询语言

多维数据分析技术在建立企业级数据仓库的过程中起着关键的作用,同时也是面向联机分析处理(OLAP)的核心.本文在分析了传统多维数据查询语言MDX(Multi-Dimensional eXpression)不足的基础上,提出了一种改进的多维数据查询的语言模型MD-SQL,并给予实现,最后对模型提出了优化方法,通过相关实验证明MD-SQL语言性能优于MDX语言.     

BI中的特殊语言——MDX（上）

MDX在多维数据库中的应用，不仅为我们提供了便利查询、获取多维数据的有效途径，还扩展了多维数据的视觉化效果，从而使我们的商业智能系统更加符合企业应用的需求，实现强大的功能。本期专栏中。我们针对能够对多数数据进行访问、操作的MDX语言，从简单的语法要素入手，以进阶的方式。介绍MDX从基础到高级的应用，以及MDX与SQL之间显著的区别。[编者按]     

基于Analysis Service 开发OLAP程序

首先阐述了开发联机分析处理程序的必要性，以及分析服务的联机分析处理环境，然后讲述了基于分析服务的编程接口，利用多维数据查询语言结合ADO／MD进行联机分析处理程序的开发过程，即联机分析处理数据库的建立，连接到多维数据库以及使用ADO／MD执行MDX查询。     

基于实例化视图的MDX语句执行性能优化

多维表达式(MDX)语句多用于实现对海量数据的多维分析,如何对MDX语句的执行过程进行优化,进而提高查询速度是构建在线分析处理系统的一个难点。该文采用预建实例化视图的方法,使得原先基于事实表和维表的多表连接查询在经过查询重写后,可直接利用单一的候选实例化视图完成,从而大大加快了MDX语句的执行速度。阐述了选取候选实例化视图的基本思路,并给出实验结果。实验证实,数据量越大、MDX语句越复杂,性能提升的效果越明显。     

基于聚类的OLAP多维分析查询推荐方法研究

为提高用户在使用OLAP(联机分析处理)时的查询效率,改善用户体验,对OLAP中的MDX(多维表达式)查询语言进行了研究,提出了在MDX查询语言的基础上,对OLAP用户的历史查询记录进行聚类的方法,建立了查询操作的分级推荐机制,提供给当前用户候选查询作为推荐,帮助用户设计下一步查询操作.介绍了该方法的推荐机制构架,并表明了该方法的可行性.     

OLAP query reformulation in peer-to-peer data warehousing

Inter-business collaborative contexts prefigure a distributed scenario where companies organize and coordinate themselves to develop common and shared opportunities, but traditional business intelligence systems do not provide support to this end. To fill this gap, in this paper we envision a peer-to-peer data warehousing architecture based on a network of heterogeneous peers, each exposing query answering functionalities aimed at sharing business information. To enhance the decision making process, an OLAP query expressed on a peer needs to be properly reformulated on the local multidimensional schemata of the other peers. To this end, we present a language for the definition of mappings between the multidimensional schemata of peers and we introduce a query reformulation framework that relies on the translation of mappings, queries, and multidimensional schemata onto the relational level. Then, we formalize a query reformulation algorithm and prove two properties: correctness and closure, that are essential in a peer-to-peer setting. Finally, we discuss the main implementation issues related to the reformulation setting proposed, with specific reference to the case in which the local multidimensional engines hosted by peers use the standard MDX language.     

数据仓库在超市中的应用

绍了数据仓库技术,以某超市销售决策支持系统为背景,详细描述了面向超市销售数据仓库的建模方法和步骤,并结合连锁超市企业的具体业务,给出了连锁销售决策支持系统的模型。利用OLAP技术构建出销售多维数据集,使用多维查询语言MDX对该多维数据集进行查询以便决策者方便地对销售量、库存情况进行分析并作出正确的决策。     

Modelling and querying geographical data warehouses

A number of proposals for integrating geographical (Geographical Information Systems—GIS) and multidimensional (data warehouse—DW and online analytical processing—OLAP) processing are found in the database literature. However, most of the current approaches do not take into account the use of a GDW (geographical data warehouse) metamodel or query language to make available the simultaneous specification of multidimensional and spatial operators. To address this, this paper discusses the UML class diagram of a GDW metamodel and proposes its formal specifications. We then present a formal metamodel for a geographical data cube and propose the Geographical Multidimensional Query Language (GeoMDQL) as well. GeoMDQL is based on well-known standards such as the MultiDimensional eXpressions (MDX) language and OGC simple features specification for SQL and has been specifically defined for spatial OLAP environments based on a GDW. We also present the GeoMDQL syntax and a discussion regarding the taxonomy of GeoMDQL query types. Additionally, aspects related to the GeoMDQL architecture implementation are described, along with a case study involving the Brazilian public healthcare system in order to illustrate the proposed query language.     

基于对象化查询的OLAP系统的研究与应用

针对多维表达式（MDX）的非结构化对增量操作的限制，提出了一个对象化的查询结构，并阐述了在该结构的基础上对分析路径的跟踪和序列化的实现。该对象结构可以使OLAP系统高效方便地实现对分析线索的维护和对分析动作的记录，提高了OLAP系统的可用性。     

关系数据库中多维数据分析及展现的研究

在对关系数据库业务模型分析的基础上通过建立多维模型定义使数据具有多维的概念,并在此基础上提供了一种B/S模式的多维数据查询和展现的方法,该方法充分利用了多种数据存储结构来优化数据的查询和显示.通过建立维度间的层次关系可实现多维数据的钻取、旋转、切片等操作,结合JFreeChart的图形功能提供了一种多维图形展现和分析的方法.     

Betriebliches Identitätsmanagement

Enterprise Identity Management Systems (EIdMS) are an IT-based infrastructure that needs to be integrated in various business processes and related infrastructures. Assessment and preparation of decisions for the introduction need to take the costs, benefits, and the organizational settings into consideration. A variety of methods for the evaluation and decision support of new IT (e. g. EIdMS) are discussed in the literature – however, these are typically based on single dimensions (e. g. financial or technology aspects). This paper proposes a multidimensional decision support framework, based on the Balanced Scorecard concept. The presented approach introduces four perspectives and a related set of initial decision parameters to support decision making. The perspectives are (a) financial/monetary, (b) business processes, (c) supporting processes and (ICT) infrastructure and (d) information security, risks and compliance. Perspectives and adaptable sets of decision parameters also may serve as foundation for software-based decision support instruments.     

一种有效的空间数据仓库区域聚集查询索引结构

空间数据仓库有效地支持对空间数据的管理和分析，提供更加全面的决策支持．讨论了一种有效的空间决策支持手段——空间区域聚集查询的实现．基于aggregate cubetree和aR—tree提出了一个可以有效地在空间维和非空间维上进行区域聚集查询的索引结构aCR-tree及其相关算法，并计算分析了查询算法的时间复杂度．与现有技术相比aCR-tree降低了存储代价和每次查询访问的节点数，通过实验证明，该索引结构可以提供较好的存储性能和查询性能．     

道路运输安全管理MDAS的设计与实现

针对目前道路运输安全管理决策缺乏针对性和有效性的问题,提出基于车载GPS监控数据,将多维分析技术应用于营运车辆GPS监控数据的分析,从而获得车辆的行驶规律,以加强安全管理。在对现有基础和条件进行分析的基础上,提出了道路运输安全管理MDAS总体解决方案,包括系统的全局网络拓扑结构和五层体系结构。设计了基于DTS和触发器的数据抽取器,分别利用概化和最近点估计地图匹配算法对非空间和空间数据进行预处理,设计了雪花MDAS数据仓库模型、MDAS数据集及基于MDX的数据分析引擎。以此为基础,开发了重庆市道路运输安全管理MDAS。应用表明,该系统符合道路运输安全管理决策支持的需求。     

基于Web的电信闭环决策支持系统的研究与实现

通过对电信数据的分析，提出了基于Web的电信闭环决策支持系统的实现方案。确定了电信领域的决策分析主题，建立了相关的联系和模型，阐述了采用基于数据仓库的四层结构的电信闲环决策支持系统的系统架构和功能。运用联机分析处理、数据挖掘等分析决策手段实现了对电信数据多角度、多层次的查询、分析及决策功能。     

PARSIMONY: An Infrastructure for Parallel Multidimensional Analysis and Data Mining

Multidimensional analysis and online analytical processing (OLAP) operations require summary information on multidimensional data sets. Most common are aggregate operations along one or more dimensions of numerical data values. Simultaneous calculation of multidimensional aggregates are provided by the Data Cube operator, used to calculate and store summary information on a number of dimensions. This is computed only partially if the number of dimensions is large. Query processing for these applications requires different views of data to gain insight and for effective decision support. Queries may either be answered from a materialized cube in the data cube or calculated on the fly.  The multidimensionality of the underlying problem can be represented both in relational and in multidimensional databases, the latter being a better fit when query performance is the criteria for judgment. Relational databases are scalable in size for OLAP and multidimensional analysis and efforts are on to make their performance acceptable. On the other hand multidimensional databases have proven to provide good performance for such queries, although they are not very scalable. In this article we address (1) scalability in multidimensional systems for OLAP and multidimensional analysis and (2) integration of data mining with the OLAP framework. We describe our system PARSIMONY, parallel and scalable infrastructure for multidimensional online analytical processing, used for both OLAP and data mining. Sparsity of data sets is handled by using chunks to store data either as a dense block using multidimensional arrays or as sparse representation using a bit encoded sparse structure. Chunks provide a multidimensional index structure for efficient dimension oriented data accesses much the same as multidimensional arrays do. Operations within chunks and between chunks are a combination of relational and multidimensional operations depending on whether the chunk is sparse or dense. Further, we develop parallel algorithms for data mining on the multidimensional cube structure for attribute-oriented association rules and decision-tree-based classification. These take advantage of the data organization provided by the multidimensional data model.  Performance results for high dimensional data sets on a distributed memory parallel machine (IBM SP-2) show good speedup and scalability.