基于OLE DB的ADO数据访问技术

介绍了基于OLE DB的ADO数据访问技术,并详细介绍了OLE DB／ADO的概念及对象模型和工作原理,还举例说明了ADO数据访问技术的一般过程和实现方法。     

两种Web数据源动态连接方法的比较

介绍了利用OLE DB、ODBC连接Web数据源的两种方法，系统分析比较两种连接方法优缺点。     

基于ASP.net的SQLServer与文本应用程序数据交换的研究与实现

SQL Server与文本应用程序之间的数据导入导出的实现主要有两种方法,一种是SQL Server本身提供的方法,另一种是通过编写客户端程序来实现数据的导入与导出功能;其中通过客户端程序来实现数据导入时采用的是ASP.net中提供的OLE DB Provider for Jet 4.0链接到Excel,将Excel数据读取到数据集对象中,数据导出是采用的是ASP.net中提供的StreamWriter类来实现的。     

基于ADO接口和VC++的数据库应用系统开发

通过一个基于ADO接口和VC 编程语言的数据库查询系统的开发过程，介绍了对OLE DB和ADO的理解，提倡使用ADO开发数据库应用系统。     

基于ASP的流行语投票系统

本文介绍了我们利用ASP可以直接在HTML文件中嵌入脚本命令用JavaScript或VBScript脚本语言编写程序以及ADO组件对任何与ODBC兼容的数据库或OLE DB数据源的高性能连接并结合数据库结构化查询语言(SQL)开发的一个流行语投票系统．     

VB6.0中利用ADO对象实现数据库编程

ADO(ActiveX Data Object)是建立在OLE DB技术上的数据对象,使用ADO能对各种类型数据库进行灵活而高效地访问。本文主要介绍了ADO的三个核心对象Connection,Recordset和Command,并用这三个对象进行了编程示例和分析。     

数据访问技术研究

数据访问技术已经成为当今许多大规模应用软件必不可少的关键技术，数据访问技术的优劣直接影响着应用软件的性能。本在介绍了ODBC、DAO、RDO等微软数据访问技术基础之上，着重介绍了OLE DB、ADO及其相互关系。     

基于VC++和0040的图文管理系统的研究

本文主要介绍基于0040(Orade object for OLE)和VC++实现快速访Oracle数据库,并在此基础上开发图文管理系统.     

基于VC＋＋和OO4O的图文管理系统的研究

本文主要介绍基于OO4O（Oracle object for OLE）和VC＋＋实现快速访问Oracle数据库,并在此基础上开发图文管理系统。     

数据挖掘与数据库的集成方法

数据挖掘的研究主要集中在挖掘算法上,但在数据库领域至关重要的数据挖掘系统与数据库的有效集成研究却很少,为此,在详细研究了数据挖掘耦合数据库的主要方法(通过SQL(Structured Query Language)游标接口读取数据、保存数据至本地磁盘cache进行挖掘、用存储过程封装挖掘算法、采用用户自定义函数表达挖掘算法以及通过扩展SQL直接操作挖掘模型)的基础上,指出在实现数据挖掘同数据库无缝集成的发展过程中,在现有的DB/DW中集成数据挖掘系统并提供应用程序和自定义挖掘算法的接口、研究推出标准数据挖掘语言是实现数据挖掘系统与数据库有效集成的关键技术。     

基于SQL的DMQL在商业数据仓库查询中的应用

介绍数据挖掘语言中的DMQL,从建立多维数据仓库模型入手,介绍了如何用基于SQL的DMQL定义此多维数据仓库模型.详细介绍基于SQL的DMQL在商业数据仓库查询中的具体应用,最后文章提到其它的数据挖掘语言:预言模型标记语言和OLEDBforDM语言.     

Research on spatial association rules mining in two direction

In data mining from transaction DB, the relationships between the attributes have been focused, but the relationships between the tuples have not been taken into account. In spatial database, there are relationships between the attributes and the tuples, and most of the associations occur between the tuples, such as adjacent, intersection, overlap and other topological relationships. So the tasks of spatial data association rules mining include mining the relationships between attributes of spatial objects, which are called as vertical direction DM, and the relationships between the tuples, which are called as horizontal direction DM. This paper analyzes the storage models of spatial data, uses for reference the technologies of data mining in transaction DB, defines the spatial data association rule, including vertical direction association rule, horizontal direction association rule and twodirection association rule, discusses the measurement of spatial association rule interestingness, and puts forward the work flows of spatial association rule data mining. During twodirection spatial association rules mining, an algorithm is proposed to get nonspatial itemsets. By virtue of spatial analysis, the spatial relations were transferred into nonspatial associations and the nonspatial itemsets were gotten. Based on the nonspatial itemsets, the Apriori algorithm or other algorithms could be used to get the frequent itemsets and then the spatial association rules come into being. Using spatial DB, the spatial association rules were gotten to validate the algorithm, and the test results show that this algorithm is efficient and can mine the interesting spatial rules.     

Research on spatial association rules mining in two-direction

In data mining from transaction DB, the relationships between the attributes have been focused, but the relationships between the tuples have not been taken into account. In spatial database, there are relationships between the attributes and the tuples, and most of the associations occur between the tuples, such as adjacent, intersection, overlap and other topological relationships. So the tasks of spatial data association rules mining include mining the relationships between attributes of spatial objects, which are called as vertical direction DM, and the relationships between the tuples, which are called as horizontal direction DM. This paper analyzes the storage models of spatial data, uses for reference the technologies of data mining in transaction DB, defines the spatial data association rule, including vertical direction association rule, horizontal direction association rule and two-direction association rule, discusses the measurement of spatial association rule interestingness, and puts forward the work flows of spatial association rule data mining. During two-direction spatial association rules mining, an algorithm is proposed to get non-spatial itemsets. By virtue of spatial analysis, the spatial relations were transferred into non-spatial associations and the non-spatial itemsets were gotten. Based on the non-spatial itemsets, the Apriori algorithm or other algorithms could be used to get the frequent itemsets and then the spatial association rules come into being. Using spatial DB, the spatial association rules were gotten to validate the algorithm, and the test results show that this algorithm is efficient and can mine the interesting spatial rules.     

ADO数据存取技术

因特网的飞速发展,使得基于Web的数据库技术变得非常重要。MicrosoftActiveXDataobjects（ADO）是以ActiveX技术为基础的数据存取技术。使用ADO,客户端应用程序能够通过任何OLEDB提供者来访问和操作数据库服务器中的数据。文章分析了ADO的工作原理,给出了ADO的使用方法,以及如何用VB和VC++两种编程语言建立数据库连接、创建和激活命令、处理和更新数据等实现ADO数据访问技术的实现方法。     

An ontology reasoning architecture for data mining knowledge management

In order to realize the intelligent management of data mining （DM） domain knowledge, this paper presents an architecture for DM knowledge management based on ontology. Using ontology database, this architecture can realize intelligent knowledge retrieval and automatic accomplishment of DM tasks by means of ontology services. Its key features include：①Describing DM ontology and meta-data using ontology based on Web ontology language （OWL）.② Ontology reasoning function. Based on the existing concepts and relations, the hidden knowledge in ontology can be obtained using the reasoning engine. This paper mainly focuses on the construction of DM ontology and the reasoning of DM ontology based on OWL DL（s）.     

一种基于XML的Web数据挖掘技术

阐述了一种基于XML的数据抽取技术 ,并示例如何将该技术应用于Web信息的挖掘 ,通过对实例的剖析 ,提出了有关技术人员如何通过该数据挖掘技术拥有一个维护成本低廉而且可靠的数据抽取系统 ,从而快速便捷地获取所需的信息