OLAP中一种多维数据模型

分析了传统OLAP(联机分析处理)模型在商业应用领域中存在的不足，在此基础上，针对某一超市的OLAP的设计过程中所碰到的问题，提出了一种多维数据模型，给出了模型定义和基于关系数据库模型实现方法。     

基于数据仓库和联机分析处理的青少年同伴关系研究

本文采用数据仓库和联机分析处理（OLAP）对南京市710名初一学生的同伴关系进行研究,研究结果表明使用数据仓库和联机分析处理技术对心理学问题的研究是可行的。新的研究方法可以和传统研究方法相互验证,为心理学数据的分析提供了另一种途径。     

浅谈数据仓库中的OLAP技术

随着数据仓库技术和联机分析处理(OLAP)技术的发展,多维数据查询与分析已经广泛应用到商务、金融以及军事等多个领域的信息处理中,为各行业的决策分析提供了强大的支持。本文主要从数据仓库及OLAP技术的相关概念,OLAP多维数据模型及核心技术方面进行了分析总结。     

Extending OCL for OLAP querying on conceptual multidimensional models of data warehouses

The development of data warehouses begins with the definition of multidimensional models at the conceptual level in order to structure data, which will facilitate decision makers with an easier data analysis. Current proposals for conceptual multidimensional modelling focus on the design of static data warehouse structures, but few approaches model the queries which the data warehouse should support by means of OLAP (on-line analytical processing) tools. OLAP queries are, therefore, only defined once the rest of the data warehouse has been implemented, which prevents designers from verifying from the very beginning of the development whether the decision maker will be able to obtain the required information from the data warehouse. This article presents a solution to this drawback consisting of an extension to the object constraint language (OCL), which has been developed to include a set of predefined OLAP operators. These operators can be used to define platform-independent OLAP queries as a part of the specification of the data warehouse conceptual multidimensional model. Furthermore, OLAP tools require the implementation of queries to assure performance optimisations based on pre-aggregation. It is interesting to note that the OLAP queries defined by our approach can be automatically implemented in the rest of the data warehouse, in a coherent and integrated manner. This implementation is supported by a code-generation architecture aligned with model-driven technologies, in particular the MDA (model-driven architecture) proposal. Finally, our proposal has been validated by means of a set of sample data sets from a well-known case study.     

以数据仓库和OLAP技术为基础的税务决策支持系统的研究和应用

本文介绍了数据仓库和ＯＬＡＰ技术的有关概念和内容,为读者了解这些技术提供了一个较全面的视图,同时,我们提供了一个才气仓库和ＯＬＡＰ技术应用实例的概貌和结构,以期读者对这些技术有进一步理解。     

Active data warehouses: complementing OLAP with analysis rules

Conventional data warehouses are passive. All tasks related to analysing data and making decisions must be carried out manually by analysts. Today's data warehouse and OLAP systems offer little support to automatize decision tasks that occur frequently and for which well-established decision procedures are available. Such a functionality can be provided by extending the conventional data warehouse architecture with analysis rules, which mimic the work of an analyst during decision making. Analysis rules extend the basic event/condition/action (ECA) rule structure with mechanisms to analyse data multidimensionally and to make decisions. The resulting architecture is called active data warehouse.     

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.     

数据仓库及OLAP技术在电信业务分析中的应用探讨

以一个对实时收入和实时话务为基础数据进行的包括诸如时间、产品、渠道、资费等的多维度分析的过程为例,论述了数据仓库及联机分析(OLAP)技术的概念、技术要点及开发实施步骤,探讨了这些技术在电信业务分析中的应用.     

OLAP查询多维数据的新模型

针对目前现有的OLAP查询机制,讨论了一种新的模型——统计树。这种模型可以应用到OLAP中,并能够有效提高OLAP查询多维数据的速度。     

Attribute value reordering for efficient hybrid OLAP

The normalization of a data cube is the ordering of the attribute values. For large multidimensional arrays where dense and sparse chunks are stored differently, proper normalization can lead to improved storage efficiency. We show that it is NP-hard to compute an optimal normalization even for 1 × 3 chunks, although we find an exact algorithm for 1 × 2 chunks. When dimensions are nearly statistically independent, we show that dimension-wise attribute frequency sorting is an optimal normalization and takes time O(dn log(n)) for data cubes of size n^d. When dimensions are not independent, we propose and evaluate a several heuristics. The hybrid OLAP (HOLAP) storage mechanism is already 19-30% more efficient than ROLAP, but normalization can improve it further by 9-13% for a total gain of 29-44% over ROLAP.     

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。     

数据仓库中多维分析的数据展现

文章介绍了一种数据仓库多维分析中以二维表格反映多维特征,提供了数据立方体的可视化表示的方法,并采用动态填充技术,提高了大数据量时数据查询、展现的速度,使用户能直观、快速地得到数据。     

Smoothing over Summary Information in Data Cubes

Decision support usuallyrequires drawing from a huge data warehouse some statisticalinformation that is interesting and useful to its users. A typicaldata model that supports the data warehouse is the multidimensionaldatabase, also known as a data cube. A data cube contains cells,each of which is associated with some summary information, or aggregate, that the decisions are to be based on. However, inreal-life databases, due to the nature of their contents, datadistribution tends to be clustered and sparse. The sparsity situationgets worse, in general, as the number of cells increases. Forthose cells that have support levels below a certain threshold,combining with adjacent cells is necessary to acquire sufficientsupport. Otherwise, incomplete or biased results could be deriveddue to lack of sufficient support.Our mainfocus in this paper is to find approximations for the missingor biased aggregates of those cells that have missing or lowsupport. We call this approximation process smoothing in thispaper. We propose a smooth function that can smooth nicely ona quantitative attribute while still being preserved locally.Our method is also adaptive to sudden changes of data distribution,called discontinuities, that inevitably occur in real-life data.     

基于数据仓库技术的决策支持系统的应用研究

数据仓库和联机分析处理是信息领域新兴的计算机技术,近年来得到了广泛的应用。本文以上海市公交一卡通工程决策支持系统为背景,给出一种基于数据仓库和联机分析处理的专用的决策支持系统的实现方案,解决了长期困扰决策支持系统的数据管理问题和观察分析问题,对现实有一定的指导作用。     

Discovering OLAP dimensions in semi-structured data

OLAP cubes enable aggregation-centric analysis of transactional data by shaping data records into measurable facts with dimensional characteristics. A multidimensional view is obtained from the available data fields and explicit relationships between them. This classical modeling approach is not feasible for scenarios dealing with semi-structured or poorly structured data. We propose to the data warehouse design methodology with a content-driven discovery of measures and dimensions in the original dataset. Our approach is based on introducing a data enrichment layer responsible for detecting new structural elements in the data using data mining and other techniques. Discovered elements can be of type measure, dimension, or hierarchy level and may represent static or even dynamic properties of the data. This paper focuses on the challenge of generating, maintaining, and querying discovered elements in OLAP cubes.We demonstrate the power of our approach by providing OLAP to the public stream of user-generated content on the Twitter platform. We have been able to enrich the original set with dynamic characteristics, such as user activity, popularity, messaging behavior, as well as to classify messages by topic, impact, origin, method of generation, etc. Knowledge discovery techniques coupled with human expertise enable structural enrichment of the original data beyond the scope of the existing methods for obtaining multidimensional models from relational or semi-structured data.     

MSMiner—a developing platform for OLAP

Since the early 1970s, decision support systems (DSS) have evolved significantly. In this paper, the design and implementation of MSMiner, a developing platform for DSS, is introduced. The system is constructed on a data warehouse and integrated with a number of data mining algorithms. It is well suited for on-line analytical processing (OLAP). The characteristics of MSMiner include the ability to support multiple data sources and data mining strategies, additional organizational flexibility in regard to data and mining strategies, and the powerful expansibility of data mining tasks.     

一种基于MS OLAP Services的决策支持系统的开发模式

给出了一种利用MSOLAPServices技术构造OLAP应用的完整设计与实施方案。它从后台数据仓库／数据集市的构建到前台客户端的编制都提出了设计与实现的方法,从而提出了建立决策支持应用后种工程应用模式。     

一种OLAP海量数据载入技术的研究

在数据仓库、数据挖掘和联机分析处理系统中,海量数据的载人虽然不是时时发生,但是海量数据的载人效率直接影响昔系统性能,如何高效地进行海量数据的载人十分重要。提出了两种技术,即基于UB—Tree的海量数据的初始化载入技术以及海量数据的增量载人技术,阐述了基于UB—Tree的海量数据载人的技术及其算法,提出了海量数据载人模型,建立基于UB—Tree的初始化载人,以及如何在已有的UB—Tree上做增量载人。经过性能分析,算法减少了I／O和CPU代价,为一种有效的海量数据载人方法。     

数据仓库与OLAP在通信线路状态统计中的应用

近年来,随着通信行业的飞速发展,宽带接入的用户越来越多。客户对带宽和宽带的质量提出更高的要求,如何降低运营成本,提升网络质量,是宽带运营商运行维护面临的最大问题。因此,企业必须对通信线路的运行维护状况有一个整体的了解。本文针对以上的问题,根据企业对通信线路状态统计的需求,通过运用数据仓库及OLAP技术,对原有的通信线路状态统计的相关数据进行多维分析,制定一套切实可行的线路状态统计方案,此方案可供通信企业在线路状态统计中参考。     

中医诊疗决策支持系统中的OLAP与DM融合

提出了一种基于数据仓库、在线分析处理及数据挖掘的中医诊疗决策支持系统的解决方窠，该方案很好地融合了中医个体化诊疗的特点。结合实际应用详细讨论了中医诊疗数据仓库建立、OLAP实现的关健问题和数据挖掘的应用主题，设计了集成化的中医诊疗决策支持系统的3层体系结构，同时给出了实现案例。