semi-structured data

Research on New Product Structure Model Based on Semi-structured Data

基于半结构化数据的新型产品结构模式研究

The structure of semi-structured data and some semi-structured data models are analyzed .

分析了半结构化数据的特点以及目前主要的半结构数据模型。

However , most of the research is based on database system and specialized semi-structured data management system .

然而，大部分研究都是基于数据库及专门的半结构化管理器之上的。

HTML represents one common form of semi-structured data .

HTML表示一种常见形式的半结构化数据。

As a special semi-structured data , XML is different from the data in relational data model .

XML作为一种特殊的半结构化数据，与关系数据模型中的数据是不同的。

Maximum Frequent Pattern Mining from Semi-Structured Data Based on Rightmost Expansion Technique

基于最右扩展枚举的半结构化数据最大模式挖掘方法研究

A Unified Integrating Method for Structured and Semi-structured Data

一种结构化数据和半结构化数据的统一集成模型

Knowledge Discovery of the Semi-structured Data of Web-based Resource

基于Web的半结构化数据的知识发现

With the rapid development of information technology , many enterprises have accumulated a large number of semi-structured data .

随着信息化技术的飞速发展，各企业和单位都积累了大量的半结构化数据。

There are a lot of semi-structured data in a large number of Internet Web pages .

分布在互联网中的Web网页数据中，存在着大量的半结构化数据。

The schema discovery of semi-structured data has become the first step of knowledge discovery .

为此，半结构化数据的模式发现就成为知识发现的首要步骤。

RDF is often used to represent semi-structured data .

RDF经常被用来表示半结构化的数据。

The information resource On Web , usually exist by semi-structured data form , special xml data form .

Web上的信息资源，通常以半结构化数据的形式，特别是XML数据的形式存在。

Storage Models and Query Execution of Semi-Structured Data

半结构数据的存储模型和查询执行

When do I use formal parsers to process structured and semi-structured data ?

什么时候能够使用正规的分析器来处理结构化和半结构化的数据？

Can you store semi-structured data in relational structures ?

能够在关系结构中存储半结构化数据吗？

The existing literature seldom covers ontology learning methods and technologies based on semi-structured data sources .

从现有的文献资料看很少专门介绍基于半结构化数据源的本体学习方法与技术的阐述。

Fast mining frequent patterns in semi-structured data stream

半结构化文档数据流的快速频繁模式挖掘

On the Key Constraints for Semi-Structured Data

面向半结构化数据的Key约束研究

Therefore , the data mining technology for the websites to tackle heterogeneous data and semi-structured data .

因此，面向Web的数据挖掘技术首先要解决异构数据源问题和半结构化的数据问题。

Semi-structured data have no unified structural characteristics , and there is correlation between the data and the structure .

半结构化数据的内容与结构相关联，没有统一的结构。

Semi-structured Data Query Based Model-Checking and Its Application

基于模型检测的半结构化数据查询及其应用

Now the management problem of XML-based semi-structured data becomes a hot topic in the field of data management .

半结构化数据管理问题成为当前Web数据处理领域的一个研究热点，其中XML描述的半结构化数据的有效存储和查询是Web数据管理的难点问题。

These attributes make XML dialects prime vocabularies for representing semi-structured data .

这些属性使得XML方言成为表示半结构化数据的主要词汇表。

Define a source view model based on XML , which has a good description for structured data and semi-structured data ;

基于XML定义数据源视图的模型规则，对于结构化数据和半结构化数据能够进行很好的描述；

This has resulted in the different type of data information , including traditional database data and other structured , semi-structured data .

这就造成了数据信息类型的不统一，其中就包括了传统数据库系统中的数据信息以及其他系统中大量的结构化、半结构化数据。

In recent years , the research about the data mining based on the unstructured and semi-structured data become one of research focuses .

针对海量非结构化与半结构化数据进行挖掘分析成为近年来研究的热点。

With the development of enterprise informatization and the Internet , the amount of unstructured and semi-structured data increase .

企业信息化与互联网的发展促使非结构化与半结构化数据不断增加。

This paper presents a new data model for web , Spatial Object Model ( SOM ), which extends semi-structured data model .

本文提出一种新的Web数据模型-空间对象模型SOM，给出了形式化描述。

However , most of the investigations of query processing and optimization are based on structured and semi-structured data types .

而在数据流系统查询处理及优化的研究中，大多面向结构化和半结构化的数据对象。