许多早期KM系统的设计，要求人们向数据库中输入材料或者创建个人档案，来帮助大家发现专门的知识和技术，从而实现组织中信息的获取。
Many early KM systems were designed to capture corporate information by requiring people to enter stuff into databases or to create personal profiles to help people find expertise.
数据挖掘是近年来企业用以分析大型数据集的核心技术，是知识发现过程中的关键步骤，是数据库技术的进一步扩展。
Data Mining is recently core technologies for an enterprise to analyze large data-sets, and it is a key step in knowledge discovery process and a database technical further expanding.
介绍了数据库知识发现(KDD)活动的展开要求，着重从它的技术处理流程来分析它的特性及其存在价值与意义。
This article introduces the KDD activity the request that launch, and puts great emphasis on its characteristic and value by technique processing.
本论文还简要讨论了在数据库中发现知识的数据可视化问题，并采用神经网络技术解决该问题，描述了建立一个神经网络数据挖掘的全过程。
Meanwhile, the paper discusses the problem of data visualization, and resolves it using neural network technique, describes the whole process of building a neural network data mining system.
空间数据挖掘技术为环境数据库的知识发现提供了有效的途径。
Spatial data mining technology offers valuable means for discovering knowledge in environmental database.
铜矿专家系统中知识库和规则库的保存和管理使用了数据库开发技术，采用数据挖掘作为知识发现的新手段。
The database development skills were applied to store and manage the knowledge and the rules while the knowledge founding adopted data mining technology.
数据挖掘是一项较新的数据库技术，它基于大量数据所构成的数据库，从中发现潜在的、有价值的信息——称为知识，用于支持决策。
Data Mining is a newer database technique which aims at discovering potential and valuable pattern that is called as knowledge. The knowledge discovered can be used for decision-making.
数据挖掘，或者叫做数据库知识发现，是一种自动在大量数据中寻找具有某种相同属性集合的技术。
Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns.
数据挖掘，或者叫做数据库知识发现，是一种自动在大量数据中寻找具有某种相同属性集合的技术。
Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns.
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