使用XML表示半结构化数据
另一方面，xml格式非常适合描述半结构化数据。
XML formats, on the other hand, are good at describing data of a semi-structured nature.
病历模板和病历内容都属于半结构化数据。
The medical record template and the content of medical record both belong to the data of half structure.
使用XML从半结构化数据集中抽取有用信息。
Using XML to extract useful information from semi-structured data sets.
模式抽取在半结构化数据研究领域中具有重要意义。
Extracting schema is important in the field of semistructured data research.
这是一个相当简单的从半结构化数据到HTML的映射。
This is a fairly simple mapping from semi-structured data to HTML.
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。
These attributes make XML dialects prime vocabularies for representing semi-structured data.
它提供了从结构化和半结构化数据源创建数据Feed的工具。
It provides tools to create data feeds from structured and semi-structured data sources.
XML基于半结构化数据模型，而半结构化数据很难统一存储和管理。
XML is based on semistructured data model, but the semistructured data is hard to store and manage.
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。
In recent years, the research about the data mining based on the unstructured and semi-structured data become one of research focuses.
Mozilla还使用资源描述框架(RDF)管理XUL中使用的半结构化数据。
Mozilla also USES Resource Description Framework (RDF) to manage semi-structured data used in XUL.
提出一种利用关系数据库系统在一般图结构的半结构化数据上进行近似查询的途径。
An approach to approximate querying of general graph structured semistructured data is proposed based on a relational database system.
提出了一种基于XML存储半结构化数据的方法，设计并实现了相应存储与解析算法。
The paper proposes an approach to storing semi-structured data based on XML, and develops algorithms for storing and parsing.
半结构化数据（主要是HTML形式的）正在开创从web挖掘数据的新局面。
Semi-structured data, primarily in the form of HTML, is enabling new prospects for mining data from the web.
关系型数据库中包含了大量的关系模式，其中的数据结构一般为结构化或半结构化数据。
In relational database, there are massive relational patterns, in which, data structure is generally structured data or semi-structured data.
如果您有一个能够存储半结构化数据的解决方案，那么也能用它存储结构化和非结构化数据。
If you have a solution that lets you store semi-structured data, you can also use it to store structured and unstructured data.
这种格式很简洁，保留了以前格式的所有信息，还保留了结构，而且仍然足够灵活，可以处理半结构化数据。
This format is simple, concise, retains all the information of earlier formats, retains structure well, yet it is still flexible enough for the semi-structured data.
提出了一种基于资源描述框架（RDF）的半结构化数据表示模型，并设计了相应的信息检索机制。
The presenting model for semi-structured data base Resource Description Framework(RDF) and its information retrieval mechanism were proposed in this paper.
本文以标记有序树作为半结构化数据的数据模型，研究了半结构化数据的树状最大频繁模式挖掘问题。
In this paper, labeled ordered tree is used as the data model of semi structured data, the problem of maximum tree structured frequent pattern mining from semi structured data is studied.
web上和内部数据存储中非结构化和半结构化数据量的爆炸式增长，促进了对对智能且高效的数据挖掘的需求。
The sheer volume of unstructured and semi-structured data found on the web and in internal data stores increases the need for intelligent and effective data mining.
半结构化数据是网络中一种重要的数据形式，其数据抽取和知识发现研究是半结构化数据各项研究的核心。
Semi-instructured data is a kind of the important type in networks, and its data extracting and knowledge discovery is the core for semi-structured researches.
半结构化数据库没有固定的库模式，用户对其结构难以产生清晰的认识，从而无法有效地查询所需的内容。
Due to the lack of stable schema in semistructured database, it is hard for users to obtain a clear view of the structure and employ efficient queries.
半结构化数据一般包含标记或其他元素，以从语义上分隔那些隐含了关于相关数据的一些详细信息的相关元素。
Semi-structured data generally contains tags or other elements to separate semantically related elements that imply some details about the associated data.
将半结构化数据表示为基于XML 的文档需要一个健壮的数据挖掘系统来支持XML消费、操纵和输出。
Representing semi-structured data as an XML-based document requires a robust data-mining system to support XML consumption, manipulation, and output.
传统的关系数据库查询语言SQL是针对平面的二维关系数据而设计的，并不适合XML/GML半结构化数据的查询；
Traditional relational database query language SQL is designed for relational tables, and not suitable for XML or GML semistructured data;
支持一种用于WebSphereTX的新数据绑定，从而允许在半结构化数据和复杂行业模式之间来回转换业务对象。
A new data binding is supported for WebSphere TX, enabling the transformation of business objects to and from semi-structured data and complex industry schemas.
本文介绍了数据挖掘、半结构化数据挖掘、XML的相关概念和研究现状，提出了一种面向XML的树型对象模型TOM。
The thesis introduces concepts and present research status about data mining, semi-structured data mining and XML, and produces an oriented-XML treelike object model named TOM.
可扩展标识语言以其合理的数据组织结构和可扩展的特性，成为各种复杂数据，特别是半结构化数据表示和处理的良好工具。
XML (Extensible Markup Language) is a good tool for representing and processing complex data, especially semistructured data for its reasonable data organization structure and extensibility.
这让数据服务可以将结构化的数据和半(或非)结构化的数据组合起来。
This allows the data service to combine structured data with semi - or unstructured data.
从当今的输出存储中挖掘数据，要求处理程序试图从通常是完全非结构化的或者最多是半结构化的数据，创建结构化的数据。
Mining data from today's data stores requires that processors attempt to create structured data from data that is often totally unstructured or semi-structured at best.
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