但是，如果您的文档是纯数据，那么将该文档引入到关系型数据库中进行操作会更有意义。
If your document is pure data, however, it still makes more sense to bring it into a relational database for manipulation.
在大多数情况下，XML文档到关系型数据的解析可通过串流方式完成，而无需太多内存且不管文档有多大。
In most cases, parsing XML documents to relational data can be done in a streaming fashion without requiring much memory and regardless of how big the document is.
由于本文档重点关注关系型数据源，以下技术可用于dmr包，也可用于olap数据源。
Since this document is focused on relational sources, the following techniques imply DMR packages, but also apply to OLAP data sources.
在哪些情况下，用面向文档的数据库优于关系型数据库？
What are some use cases for using a document-oriented database over the relational one?
考虑到一个文档对应rdbms(关系型数据库管理系统)中表的一行，一个字段对应表的那一行的某一列。
Think of a Document as a row in a RDBMS, and Fields as columns in that row.
一个数据源可以四像关系型数据库管理体统这样的数据库,可以是面向对象型的数据库管理系统,可以是XML文档,也可以是简单文件等等.
A data source could be a database such as an RDBMS, OODBMS, XML repository, flat file system, and so forth.
Wikipedia对它的定义是，提供了用于抽取和操纵来自于XML文档或者任何可以视作XML的数据源（比如关系型数据库或者office文档）中数据的方式。
Wikipedia defines it's function as providing the means to extract and manipulate data from XML documents or any data source that can be viewed as XML, such as relational databases or office documents.
对于混合型内容（文档），使用XML：如果需要使用元数据（比如URL）或标记和文本的各种混合形式，比如字处理文档和 blog文章，那么就使用 XML。
XML for mixed content (documents): If what you will post requires the use of metadata (such as URLs) or a varied mix of markup and text, such as word processing documents and blog posts, use XML.
该文档不适合存储在关系型数据库中，但是，XQuery却非常擅长此道，它能直接从该XML 文档中抽取出量化信息。
That document would be ill suited to storage in a relational database, but XQuery would do a good job of extracting the quantified information from the XML document directly.
MongoDB是一种NoSQL数据库，不同于SQLServer这样的关系型数据库，MongoDB中数据的基本单位是文档。
MongoDB is an example of a NoSQL database. Unlike a relational database such as SQL Server, the basic unit of data in MongoDB is the document.
我最近还写了Google的Bigtable相关内容，它不是一种关系型或面向文档的数据解决方案(且它偶尔不支持JDBC)。
I have also recently written about Google's Bigtable, which isn't a relational or document-oriented data solution (and, incidentally, doesn't support JDBC in any way).
此模型扩展定义了4种持久化dom节点类型，设计了一种存储混合型XML文档的方法，并依据文档的次序建立了数据聚集。
This model has defined 4 extra persistent DOM node types, designed a storing method of mixed structure XML documents and created clustered data based on XML document orders.
针对密集型数据查询要消耗大量内存的缺陷，设计了一种基于流的XM L文档查询算法。
A large amount of memories are consumed during dense data querying. A query processing algorithm based on XML stream is designed.
采用高津托图作为虚拟文件夹的数据存储结构，以树型结构作为文档分类的形式。
It adopts the Gozintograph as the data storage structure of virtual folder, and the tree structure as the sort form of document.
在此前提下，通过对公文管理模块进行设计、分析与研究，说明文档型数据库在复杂信息和流程下对信息的处理机制。
Then, archives module is framed, analyzed and researched, that illuminate the principle which is incarnated by information and workflow of document database.
本文论述、分析和比较了基于关系型数据库的XML数据的文档存储模式和元素存储模式。
The paper discusses, analyzes, compares two kind of XML data storage patterns based on relational database document pattern and element pattern.
它面向文档型数据库，实现了文档驱动的工作流管理。
It is developed for documental database, and implements document-driven workflow management.
由于历史的原因，企业数据一般由文件、关系型数据库、文档型数据库等构成。
For the historical reasons, corporate data are commonly kept in the forms of file, relation database and document database, etc.
然而由于复杂的树形XML文档和简单平坦的关系型数据库表结构之间固有的不匹配，使得XML到关系型数据库的映射实现十分困难，因此成为当前的一个热门研究课题。
However, due to the mismatch between the XML's tree structure and flat relational tables, mapping XML document to relational database is an involved problem. So it becomes a pop research direction.
然而由于复杂的树形XML文档和简单平坦的关系型数据库表结构之间固有的不匹配，使得XML到关系型数据库的映射实现十分困难，因此成为当前的一个热门研究课题。
However, due to the mismatch between the XML's tree structure and flat relational tables, mapping XML document to relational database is an involved problem. So it becomes a pop research direction.
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