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IDS and TSM together build a powerful team to back up large databases safely and to restore them quickly. IDS和TSM共同建立了一个功能强大的团队来安全备份大型数据库并快速还原它们。
This feature is useful mainly for large databases to circumvent file size limitations. 此特性主要用于帮助大型数据库克服文件大小限制。
Data mining is frequently described as the process of extracting valid and actionable information from large databases. 数据挖掘通常被称为“从大型数据库提取有效、可行信息的过程”。
No need for large database system. 不需要大型数据系统。
Data mining can be regarded as a collection of methods for discovery useful pattern from large databases. 数据开采是利用现代统计学知识和计算知识从大型数据库中发现潜在的有用模式的学科。
Data mining is used to draw interesting information from Very Large DataBases (VLDB). 数据挖掘用于从超大规模数据库中提取感兴趣的信息。
F.Masseglia,P.Poncelet,M.Teisseire.Incremental Mining of Sequential Patterns in Large Databases. 邹翔;张巍;蔡庆生;王清毅.;大型数据库中的高效序列模式增量更新算法
Large databases have their uses, doing away with paperwork and speeding things up. 大的数据库系统较方便使用，可以实现无纸化办公，并提高效率。
It also has been recognized that multiprocessor system architecture is most scalable to support very large databases. 而多处理器的平行系统，也是被公认最具扩充性，能支援大型资料库的使用需求。
Potential for false positive identifications from large databases through tandem mass spectrometry. 详细阐述质谱鉴定过程，以分析假阳性产生的来源。
Multi-core Support: REAL SQL Server takes advantage of all available processors, providing greater scalability for large databases. 多内核支持:REAL SQL Server利用了所有可用的处理器,为大型数据库提供了更大的可量测性。
For enterprises with large databases, using many backup devices can greatly reduce the time taken for backup and restore operations. 对于具有大型数据库的企业,使用多个备份设备可以明显减少执行备份和还原操作所花的时间。
When sizing, make sure you take into consideration the extra cost of large databases and frequency of full-text searches. 在调整时,一定要考虑大型数据库的额外开销和进行全文搜索的频率。
When a file server is used on a LAN,large databases are stored on the server and users may store all of their work flies there as well. 当文件服务器用于局域网时，大型数据库就被存储在服务器上，而且用户也可以将其所有的工作文件存放在上面。
This paper introduces ESMA based on sampling methods presently,a new sampling-based algorithm for discovering association rules in large databases. 在分析现有采样方法的基础上,提出了一种新的基于采样的高效关联规则挖掘算法ESMA。
Data Mining is a process of extracting valid, previously unknown, comprehensible, and actionable information from large databases. 数据挖掘是从大规模的数据中抽取出非平凡的、隐含的、未知的、有潜在使用价值的信息的技术。
When a file server is used on a LAN, large databases are stored on the server and users may store all of their work flies there as well. 当文件服务器用于局域网时，大型数据库就被存储在服务器上，而且用户也可以将其所有的工作文件存放在上面。
In this paper we give an archetypal design for data mining system, Incremental updating technique is applied in this archetypal system which quickly dealing with large databases. 摘要本文设计并实现了一个数据挖掘原型系统，并将增量更新技术应用于此原型中。
A.Savasere, E.Omiecinski, and S.Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databases”, Proc. of 21st VLDB, pp.432-444, 1995. 陈彦良、陈家仁，在限定项目个数与交易长度的资料库中挖掘关联规则，国立中央大学资讯管学系硕士论文，民国90年。
It is about creating computers that have both reasoning power and access to large databases, such that computers themselves can perform the desired task. 它所关于创造出的计算机是既要能推理，又可以能访问大型数据库，从而计算机自身可以完成人们希望中的任务。