Summary tables are an important part of creating a high-performance data warehouse.
汇总表是创建高性能数据仓库的重要部分。
This edition represents the ultimate in data warehouse performance and scalability.
此版本是数据仓库性能和可伸缩性的最终版本。
If at all possible, the initial sizing should also investigate volume and performance aspects related to populating the data warehouse.
如果完全可能，初始的大小调整还应调查与填充数据仓库相关的容量和性能方面的情况。
Data clustering can have an especially large impact on data warehouse query performance, because rows are often retrieved in large Numbers.
数据聚簇对于数据仓库查询性能的影响尤其显著，因为常常在一个查询中获取许多行。
You would prefer not to (or can't wait to) move performance data to a data warehouse and run queries and analytics against it.
您肯定不希望（或无法等待）将性能数据移动到数据仓库，然后再对它运行查询和分析。
There are many topics not covered here that are also fundamental in delivering a good data warehouse solution, including system and database design, administration, performance tuning, and others.
有许多主题未在本文中进行介绍，但它们同样是交付良好数据仓库解决方案的基础，包括系统和数据库设计、管理、性能调优等。
To manage business performance of the supply chain and to further optimize it, a powerful reporting solution needs to sit on top of the data warehouse.
在数据仓库之上部署一个强大的报告解决方案，从而管理供应链的业务绩效并进一步优化它。
The design of a data warehouse database is focused on query performance.
数据仓库数据库设计的重点在于查询性能。
New features enhance OLTP data security, OLTP performance, data warehouse performance, and administration.
新特性增强了OLTP数据安全性、OLTP性能、数据仓库性能和管理。
Storing all credit card transaction data -- current and outdated, core and related -- in the warehouse negatively impacts the performance.
将所有信用卡事务数据——当前的和过期的、核心的和相关的——存储在数据仓库中会对性能造成负面影响。
Sequential access, as exhibited by data warehouse activities, does not produce as great a performance improvement with SSDs as does random access.
在数据仓库活动所用到的顺序存取中，SSD的性能提升没有随机访问那么大。
The analytics components, including the key parts of a Data Warehouse (DW) system and a business performance reporting application.
分析组件，包括数据仓库(DW)和业务绩效报告应用程序的关键部分。
Rather than "throwing hardware at the problem" of scaling a system, TwinFin is designed in-balance from the ground up to provide scalable performance for data warehouse and analytics applications.
相比在扩展一个系统时“通过硬件解决问题”，TwinFin旨在从头到尾实现平衡，为数据仓库和分析应用程序提供可扩展性能。
The following sections describe three of these features in an OLTP context; however, each of these features will improve the performance of decision support and data warehouse systems as well.
以下部分描述了OLTP环境中的3个特性；然而，每一个特性也都将提高决策支持和数据仓库系统的性能。
Leverage historical data in the performance warehouse to analyze trends, and plan for growth.
利用性能数据仓库中的历史数据分析趋势并为增长制定计划。
IWA is an exciting breakthrough-combining new data warehouse technology with traditional Informix relational database servers-that results in very fast performance.
IWA是一个令人振奋的突破，它将新的数据仓库技术和传统的Informix关系数据库服务器相结合，产生一种非常快的性能。
Performance Warehouse saves historical monitoring data consisting of DB2 snapshot data, DB2 event monitor data, and configuration data over a long period of time (long-term history).
Performance Warehouse保存的历史监控数据由较长时间的(长期历史)DB 2快照数据、DB2事件监控数据和配置数据组成。
Using the Performance Warehouse, you can create scheduled reports on the long-term data using the Process function, or you can use queries or rules of thumb to analyze the data.
通过使用Performance Warehouse，可以使用Process功能在长期数据上创建调度的报告，还可以使用查询或经验规则分析数据。
The other type of data stored in the Performance Warehouse is that which is collected by the statement event monitors you can run from within DB2 PE.
存储在Performance Warehouse中的其他类型的数据是指那些由可以从DB2PE内部运行的语句事件监视器所收集的数据。
The Informix warehouse Accelerator really raises the performance level that we can get and expect from a data warehouse.
InformixWarehouseAccelerator提高了我们可以得到的性能水平以及我们期待从数据仓库中获得的性能水平。
The data model is the nuclear part of the data warehouse system, its quality influences the systematic application result directly and expands performance in the future.
数据建模。数据模型是数据仓库系统的核心部分，它的好坏直接影响到系统的应用效果以及未来扩展性能。
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。
According to the characteristics of data warehouse users, analyses and discusses a few approaches and techniques in order to effectively enhance and improve data warehouse performance.
根据使用数据仓库用户群的特点，分析和探讨了提高数据仓库性能的几种途径和方法。
As data warehouse became an important tool of decision making, how to improve the comprehensive performance of data warehouse is desiderated.
随着数据仓库逐渐成为企业决策支持的重要技术手段，提高数据仓库的综合性能问题日益成为人们重视与关注的焦点之一。
Therefore, improving the OLAP query performance becomes the key issue in the field of data warehouse.
因此，提高联机分析处理的查询性能就成为了数据仓库领域的关键问题。
Through researching on the origin of dormant data, the identification of them, and how to do with them these three problems in this paper, we get a good effect on the performance of data warehouse.
本文从休眠数据的产生、识别和处理三个方面进行研究，取得较好效果并维持数据仓库的性能不会下降。
Performance property plays a vital role in guaranteeing the quality of data warehouse information model and system design.
性能属性在保证数据仓库模型建设和数据仓库系统建设的质量中扮演关键角色。
In order to improve query performance, data warehouse usually contains pre-aggregation.
数据仓库通常都包含预聚集。
The performance of the query restrained the using of spatial data warehouse severely.
空间数据仓库的查询性能严重限制了空间数据仓库的使用。
The performance of the query restrained the using of spatial data warehouse severely.
空间数据仓库的查询性能严重限制了空间数据仓库的使用。
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