DWE integrates core components for warehouse administration, data mining, OLAP and inline analytics and reporting.
DWE集成了用于仓库管理、数据挖掘、OLAP和内联分析和报告的核心组件。
DB2 DWE integrates core components for warehouse administration, data transformation, and data mining, as well as OLAP analytics and reporting.
DB 2DWE集成了用于仓库管理、数据转换、数据挖掘以及OLAP分析和报告的核心组件。
InfoSphere Warehouse also includes a taxonomy Editor that categorizes dictionary entries in a taxonomy tree for use in data mining and OLAP.
InfoSphereWarehouse还包含一个TaxonomyEditor，它可以把词典条目分类为分类法树，可以供数据挖掘和OLAP使用。
InfoSphere Warehouse data mining is built with DB2 stored procedures and user-defined functions for high-performance in-database execution, taking advantage of DB2 as an execution environment.
InfoSphereWarehouse数据挖掘是用DB 2存储过程和用户定义函数构建的，以利用DB 2作为执行环境，从而获得高性能的数据库内执行。
InfoSphere Warehouse contains highly efficient implementations of almost all current data mining algorithms.
InfoSpherewarehouse几乎包含目前所有数据挖掘算法的极为高效的实现。
Thus, if the data warehouse is used for data mining, a low level of detailed data should be included in the model.
因此，如果数据仓库是用于数据挖掘的，就应该在模型中包含较低细节级(levelof detail)的数据。
InfoSphere Warehouse provides data mining functionality directly in the underlying DB2 database where the data resides.
InfoSphere Warehouse直接在存储数据的底层DB 2数据库中提供数据挖掘功能。
InfoSphere Warehouse design Studio is the Eclipse-based tooling platform used to design workload rules, data transformation flows, and analytical flows for data mining and text analytics.
InfoSphere WarehouseDesignStudio是基于Eclipse的工具平台，用于为数据挖掘和文本分析设计工作负载规则、数据转换流和分析流。
See the InfoSphere Warehouse documentation for instructions on how to see the UIMA logs of a mining or data flow and how to change the UIMA trace level to get more information.
查看InfoSphereWarehouse文档了解如何查看挖掘流或数据流的uima日志，以及如果更改uima跟踪级别获得更多信息。
Data Mining, also referred to as Knowledge Discovery from database, is to abstract the potential, unknown and useful information or pattern from the large database or data warehouse.
数据挖掘，又称数据库中的知识发现，是指从大型数据库或数据仓库中提取隐含的、事先未知的、潜在有用的信息或模式。
This topic in development USES data warehouse and data mining technology.
本课题在开发中使用数据仓库及数据挖掘技术。
Based on introduction in database technology, the paper discusses the data processing technology of goods, especially the data mining technology applying in a high rack warehouse.
在介绍数据库技术的基础上，深入讨论了货物的数据处理技术，尤其是数据挖掘技术在立体仓库中的应用。
Take data warehouse as foundation, with the aid of on Line Analytical Processing and data Mining technology, the composition and design of enterprise Decision Support System are put forward.
本文以数据仓库为基础，与目前流行的联机分析处理、数据挖掘等技术相结合，提出了基于此的决策支持系统的设计。
The comprehensive integrated DSS, which composed of data warehouse and OLAP, data mining, model base and knowledge base system, is a more sophisticated form of DSS.
将数据仓库、OLAP、数据挖掘、模型库、知识库系统结合起来形成的综合集成决策支持系统是更高形式的决策支持系统。
The circumstances of rich data and lack of knowledge lead to the emergence of technology of data warehouse and data mining, and arouse the interests of people in various fields.
数据丰富而知识贫乏的状况导致了数据仓库和数据采掘技术的出现，引起了许多不同领域的人们的极大关注。
Several issues related with Tax decision making are studied and applied, including data warehouse(DW), online analyses and processing (OLAP) and data mining (DM).
笔者对涉及到税务支持的几个关键问题进行了理论探讨和实际应用，包括数据仓库（DW）的建立和组织、联机分析（OLAP）和数据挖掘（DM）。
Data warehouse and data mining technology is one hot problems of information technology research at present.
数据仓库和数据挖掘技术是目前信息技术研究的热点问题之一。
Data warehouse and data mining techniques are key factors of decision supporting system.
数据仓库和数据挖掘技术是决策支持系统的关键因素。
The data mining is a new technology that can distill hidden, predictive information from a large database or data warehouse.
数据挖掘是一种从大型数据库或数据仓库中提取隐藏的预测性信息的新技术。
From the point of view of data, this article discusses the concept of data driven decision support system, its connotation, as well as data warehouse, on line Analytical Processing and data mining.
从数据的观点出发，讨论了数据驱动的决策支持系统的概念及其内涵，对数据仓库、联机分析处理和数据挖掘等手段也进行了一定程度的讨论。
Moreover, we research the application of OLAP and data mining technology in the data warehouse.
并研究了OLAP及数据挖掘在该数据仓库中的应用。
Technology and application of data warehouse and data mining are discussed.
对数据仓库和数据挖掘技术及其应用进行了讨论。
This paper introduces the basic knowledge of data warehouse and its analytical tools: OLAP and data Mining.
介绍了数据仓库的基础知识，以及相关的分析工具：OLAP和数据挖掘。
This paper puts forward a higher education decision support system (DSS) based on the research of data warehouse, data mining technique and decision support system.
本文通过对数据仓库、数据挖掘技术和决策支持系统的研究，提出了基于数据挖掘的高校管理决策支持系统。
Data mining technology is an effective method which draw messages or modes from the large-scale database or data warehouse that are implicit or have potential values.
数据挖掘技术是从大型的数据库或数据仓库中提取隐含的有潜在价值的信息或模式的一种有效方法。
This paper gives a comprehensive introduction about the basic ideas of data warehouse and data mining, key techniques, and the content of the main researches.
对数据仓库和数据采掘的基本概念、关键技术以及主要研究内容作了一个综合性的介绍，并讨论了数据仓库和数据采掘相结合的特点和发展潜力。
Based on data mining techniques in the data warehouse, this paper discusses how to obtain the information about products and customers effectively for the enterprise decision.
本文基于数据仓库中的数据挖掘技术，针对企业决策问题，讨论了如何有效地获取有关顾客信息及商品信息，以辅助决策者制定决策方案。
Data warehouse, data mining technology have growed up with the data contained in the information system becoming bigger and bigger.
数据仓库、数据挖掘技术正是在这种行业数据海量积累，客户数据分析需求增长的情况下应运而生的。
The main research of this text is to study data warehouse and data mining, and how to application.
本文主要研究数据仓库、数据挖掘等技术在电信行业经营分析中应用的相关理论和实际应用。
The main research of this text is to study data warehouse and data mining, and how to application.
本文主要研究数据仓库、数据挖掘等技术在电信行业经营分析中应用的相关理论和实际应用。
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