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。
The surge of social network analysis also makes graph (network) data management become one of the hottest research topic within the database and data mining community.
社会网络分析的兴起也使得图(网络)数据管理成为了当前数据库和数据挖掘领域的一个研究热点。
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。
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。
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。
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。
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。
It discusses the key technologies of application of knowledge management for CIMS in process industry, which includes Data Mining, Searching Engine and the construction of knowledge Database.
探讨了流程工业CIMS中实现知识管理系统的关键技术，它包括数据挖掘技术、搜索引擎技术和知识库的建立等。
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。
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。
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。
There were problems in traditional parallel algorithms for mining frequent itemsets more or less: data deviation, large scale communication, frequent synchronization and scanning database.
传统的挖掘频繁项集的并行算法存在数据偏移、通信量大、同步次数较多和扫描数据库次数较多等问题。
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。
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。
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。
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。
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。
This article proposes a data sorting method via the EM algorithm, for the purpose of mining high-quality decisions by performing data reasoning in a database with incomplete, noisy and uncertain data.
针对存在不完整、含噪声和不确定数据的数据库，通过挖掘高质量的决策，对数据库的数据进行推理，提出了一种基于EM算法的数据清理方法。
Data Mining is a new technology which appeared in recent years. It combines with machine learning, statistics, database and many other fields' technologies.
数据挖掘是近年来出现的一种综合了机器学习、统计学、数据库等众多领域的新技术。
The results show that data mining techniques have an extensive application prospect and it is feasible to employ data mining techniques for analyzing aero-engines test-driving database.
本文研究结果展示了数据挖掘方法在发动机试车领域广阔的应用前景，数据挖掘技术用于发动机试车数据库是切实可行的。
This paper introduces digital reference service and data mining, and expounds the design flowchart of digital reference service system based on data mining and the design of database structure.
介绍了数字参考咨询和数据挖掘，论述了基于挖掘技术的数字参考咨询系统设计框图及数据库结构设计。
In addition, the data is dynamic in a good many existing areas, such as data mining, large database and Internet information processing, etc.
另外，在现有的诸多领域，如数据挖掘、大型数据库和互联网信息处理等，其数据都是动态的。
Data mining is the process that extracts hidden, unknown and the potential value of information and knowledge form large amounts of data of the database.
数据挖掘是从数据库的大量数据中提取隐含的、未知的并有潜在价值的信息和知识的过程。
Recently the study on data mining of time series mainly concentrates on both the similarity search in a time series database and the pattern mining from a time series.
时间序列存在于社会的各个领域，对于时间序列数据挖掘的研究目前主要集中在相似性搜索和模式挖掘上。
In this paper, the sample database for data mining is designed by analyzing historical data of raw cotton property and spinning yarn quality.
通过对原棉性能及成纱质量历史数据的分析，建立了用于数据挖掘的样本库。
In this paper, the sample database for data mining is designed by analyzing historical data of raw cotton property and spinning yarn quality.
通过对原棉性能及成纱质量历史数据的分析，建立了用于数据挖掘的样本库。
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