CineCubes: Aiding data workers gain insights from OLAP queries

In this paper we demonstrate that it is possible to enrich query answering with a short data movie that gives insights to the original results of an OLAP query. Our method, implemented in an actual system, CineCubes, includes the following steps. The user submits a query over an underlying star schema. Taking this query as input, the system comes up with a set of queries complementing the information content of the original query, and executes them. For each of the query results, we execute a set of highlight extraction algorithms that identify interesting patterns and values in the data of the results. Then, the system visualizes the query results and accompanies this presentation with a text commenting on the result highlights. Moreover, via a text-to-speech conversion the system automatically produces audio for the constructed text. Each combination of visualization, text and audio practically constitutes a movie, which is wrapped as a PowerPoint presentation and returned to the user.     

OLAP技术在大型企业组织人事管理系统中的应用

介绍了OLAP技术在大型企业组织人事管理系统中的应用,并阐述了使用OLAP软件工具的目的以及系统采用的分布式数据库及OLAP技术的优点.     

分布式数据库及OLAP技术在大型企业人事系统中的应用

管理信息系统 (MIS)是在工业先进国家中从 2 0世纪 70年代以来迅速发展起来的一项综合性科学技术 ,是企业不断提高管理与决策水平 ,参与国际大市场竞争的主要手段。讨论了将分布式数据库系统与OLAP相结合的系统配置在大型企业人事系统中的实现     

数据仓库、OLAP、数据挖掘之间的关系

通过对数据仓库、联机分析处理(OLAP)、数据挖掘技术在决策支持系统中相互关系的研究,认为数据仓库和OLAP之间,以及数据仓库和数据挖掘之间存在着单向支持的关系,提出了基于数据仓库的数据挖掘系统的原型框架设计;数据挖掘、OLAP存在着双向联系,即数据挖掘为OLAP提供一定的分析模式,OLAP可对数据挖掘的结果进行技术验证,并对数据挖掘给予一定的数据属性的提示.     

基于Web的企业营销决策支持系统数据仓库的构架

面对日益激烈的市场竞争,企业营销管理与决策越来越受到重视.给出了基于Web的数据仓库的设计方案,通过数据转换、多维数据集的建立,实现了基于Web的企业营销决策支持系统的数据仓库.     

基于数据仓库的煤炭企业报表系统的设计与实现

将数据仓库技术应用于报表系统,从大量的运营数据中提取出关键数据,同时发现有用的信息,对于帮助企业管理人员制定正确的决策,提高企业整体竟争力有着重要的意义。针对某煤炭企业对制作报表所提出的需求,设计并开发了基于数据仓库技术的出帐分析报表系统,其核心部分就是构造数据仓库。     

OLAP在连锁超市中的应用研究

在OLAP技术的基础上,针对我国大、中型连锁超市的特点,提出了数据仓库多维分析模型设计方法,并用多维立方体查询语言进行数据的查询与操作,为数据仓库的数据分析提供了必要的基础。     

Specifying OLAP Cubes on XML Data

On-Line Analytical Processing (OLAP) enables analysts to gain insight about data through fast and interactive access to a variety of possible views on information, organized in a dimensional model. The demand for data integration is rapidly becoming larger as more and more information sources appear in modern enterprises. In the data warehousing approach, selected information is extracted in advance and stored in a repository, yielding good query performance. However, in many situations a logical (rather than physical) integration of data is preferable. Previous web-based data integration efforts have focused almost exclusively on the logical level of data models, creating a need for techniques focused on the conceptual level. Also, previous integration techniques for web-based data have not addressed the special needs of OLAP tools such as handling dimensions with hierarchies. Extensible Markup Language (XML) is fast becoming the new standard for data representation and exchange on the World Wide Web. The rapid emergence of XML data on the web, e.g., business-to-business (B2B) e-commerce, is making it necessary for OLAP and other data analysis tools to handle XML data as well as traditional data formats.Based on a real-world case study, this paper presents an approach to specification of OLAP DBs based on web data. Unlike previous work, this approach takes special OLAP issues such as dimension hierarchies and correct aggregation of data into account. Also, the approach works on the conceptual level, using Unified Modeling Language (UML) as a basis for so-called UML snowflake diagrams that precisely capture the multidimensional structure of the data. An integration architecture that allows the logical integration of XML and relational data sources for use by OLAP tools is also presented.     

一种高效流立方体结构

流立方体是一种通过H-tree结构实现的,通过H-cubing算法计算每个立方单元格的立方体结构。由于H-tree中的子节点是无序的,H-cubing算法的局限性导致其不能有效地进行数据流的查询和在线分析以及等高级操作。针对这一问题,提出一种新的基于ANH-tree的流立方体实现方法,该方法在H-tree的基础上,使用平衡二叉树索引无序节点并在相关节点直接建立链接来加快节点访问速度和立方单元格的计算速度,并在此基础上给出了与新结构对应的创建和查询算法,实验表明ANH-tree结构在CPU时间和内存空间等方面的性能远远优于H-tree。     

Spark环境下基于多维布隆过滤器的星型连接算法

为了适应联机分析处理(OLAP)系统中实时数据高性能分析需求不断提高的需求,提出一种能够适合Spark环境并结合多维Bloom Filter(MDBF)的星型连接算法SMDBFSJ。首先,根据多个维表构建MDBF,利用其占用空间小的特点,广播到所有节点;然后,在本地节点完成事实表过滤操作,事实表不需要在节点间移动数据;最后,过滤后的事实表与维表采用重划分方式进行连接,进而得到最终结果。SMDBFSJ算法避免了事实表数据移动,通过MDBF减小了需要广播的数据量,充分结合了广播连接和重划分连接的优势。实验结果表明了该算法的有效性,在单机和集群环境下,该算法相比重划分连接均获得了3倍左右的性能提升。