Data mining seeks to extract patterns from large sets of data using, among other things, statistical methods, artificial intelligence, and standard database management techniques.
数据挖掘旨在使用统计方法、人工智能和标准的数据库管理技术等等，从大型数据集中抽取模式。
On the other hand, if you're working with a huge data set (data mining, or database operations), having access to a much larger data cache may quite easily make up for this.
另外一方面，如果您要处理大量数据集(数据挖掘或数据库操作)，访问更大的数据缓存，那么对于64位模式来说这非常容易。
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 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。
This system adopts data mining technique to obtain useful information and knowledge from existing products database, knowledge storehouse and regulation storehouse to support conceptual design.
本系统采用数据挖掘技术从已有的产品数据库、知识库和规则库中获取有用信息和知识以支持产品概念设计。
The resulting XML data mining model is stored in the database and can be accessed through SQL/XQuery.
这样得到的XML数据挖掘模型存储在数据库中，可以通过 SQL/XQuery进行访问。
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。
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 system adopts data mining technique and obtains useful information and knowledge from existing products database, knowledge storehouse and regulation storehouse to support conceptual design.
采用数据挖掘技术从已有的产品的数据库、知识库、规则库中获取有用的信息和知识来有效地支持机械产品概念设计。
In the 21st century of the knowledge and economic time and facing with the fact of bursting data but poor knowledge, data mining has been put forward and applied in many fields of database management.
21世纪是知识经济的时代，面对数据爆炸而知识贫乏的现实，人们提出数据挖掘思想，并将其广泛应用到数据库管理的各个领域。
Data Mining, also known as knowledge Discovery in Database, distills knowledge from a mass of data.
数据挖掘就是从海量数据中提取知识，又被称为数据库中的知识发现。
Data Mining is one of the international advanced directions in the field of database and information decision.
数据挖掘是目前国际上数据库和信息决策领域的最前沿研究方向之一。
Finally, based on researches above, we implement the data mining system of the stock with aggregation database and object oriented technology.
最后，在以上研究的基础上，集合数据库、面向对象等技术实现了股票数据挖掘系统。
Data mining technology can help us discover useful schema from great capacity of data, and has become a new hotspot in database research field.
数据挖掘技术，可以帮助我们从海量数据中发现有用的模式，已经成为数据库研究的一个新的热点。
The knowledge discovery and data mining tool display their strong points in handling the great capacity database.
知识发现及数据挖掘工具在处理海量数据库时显示了它们的长处。
Typical data mining approaches look for patterns in a single relation of a database.
传统的数据挖掘算法是在数据库的一张单一的表中查找模式。
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。
Descriptive mining tasks characterize the general properties of the data in the database.
描述性的数据挖掘任务用于特征化数据库数据的一般属性。
Discovering association rules between items in a large database is an important data mining problem as the number of association rule is usually very larger.
在大型数据库项目之间发现关联规则是一个重要的数据挖掘问题，而挖掘出的关联规则数目常常是巨大的。
Clustering is a data mining problem that has received significant attention by the database community.
聚类作为数据挖掘的一个问题已经受到了数据库团体的密切关注。
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。
Data mining is a theory forward in the field of database and decision-making information, It is core of the knowledge discovery.
数据挖掘是数据库和信息决策领域的一个理论前沿，是知识发现的核心部分。
So data mining for a paging system user database in Guangdong province is presented by concept description method.
为此，论文运用概念描述的方法对广东省某寻呼台的用户资料库进行了数据挖掘。
Applying rough theory in data mining field can improve the analyzing and learning ability for incomplete data of large database, which has extensive applied prospect and applied value.
将粗糙集应用于数据挖掘领域，能提高对大型数据库中的不完整数据进行分析和学习的能力，具有广泛的应用前景和实用价值。
When mining large databases, the data extraction problem and the interface between the database and data mining algorithm become become issues.
对大型数据库进行数据开采时，数据抽取问题及数据库和开采算法的接口设计就变得十分重要。
In the dynamic increment database, data mining models of consistent and inconsistent decision system are formulated.
在增量式动态数据库中，提出了相容性和不相容性决策系统的数据挖掘模型。
However this process is quite tough. Here our statistical information database by data mining is used for predicting protein secondary structure.
本文中，我们利用数据挖掘得到的统计信息数据库对蛋白质的二级结构进行了预测。
The data extracted from the mass of knowledge and information are the major problems in data mining, processing database of scale unceasingly expands, the main methods to information messy.
从海量的数据中提取知识和信息是数据挖掘解决的主要问题、对处理数据库的规模不断扩大，而导致信息杂乱的主要方法。
At present, outlier data mining is a hotspot for the researchers of database, machine learning and statistics.
目前，离群挖掘正逐渐成为数据库、机器学习、统计学等领域研究人员的研究热点。
After studying the analysis and comparison of the realization techniques of spatial database system and spatial data mining systems, we propose a development model of spatial data mining system.
在对空间数据库系统实现技术及空间数据挖掘系统等进行比较分析的基础上，提出了一种空间数据挖掘系统的实现模式。
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