这种技术通常需要自己编写自定义的应用程序逻辑从XML文档中提取数据，然后放到关系数据库的表列中。
Often the technology in question is custom application logic that you wrote yourself to extract data from an XML document and put it into a column on a table in a relational database.
下面的图表列出了此时间间隔内运行的所有进程以及每个进程的总计的统计数据。
"The chart below that lists all the processes that ran during the interval, with the statistics accumulated for each process."
这个查询返回定时的表列数据，理解起来并不难。
This returns regular, tabular data and is not too complicated to understand.
注意它们包含了与数据库表列，以及设置的主关键字、关系相对应的区域(图19)。
Notice that they contain fields corresponding to the database table columns and that the primary keys and relationships have been set (Figure 19).
用数据库表中对应的列，对代表列的容器管理字段进行定义。
Defines the fields that are container managed with the appropriate columns in the database table that they represent.
借助XML数据类型，您可以在类似于其他数据类型的数据库表列中存储XML文档。
With the XML data type, you can store an XML document in a database table column similar to the other data types.
这有助于加快与数据库表列相关联的开发的重复性部分。
These did help speed up those repetitive parts of development that hook to your database table columns.
这类备份脚本背后的逻辑很简单：从数据库取得一个表列表，迭代此列表并使用如DESC ？
The logic behind such a backup script is quite simple: obtain a list of tables from the database, iterate over this list and use commands such as DESC?
movies表列举他商店中每部电影的相关数据，即如每部电影的名称和导演等内容。
The MOVIES table lists the pertinent data for each movie in his shop, namely things like the title and director for each movie.
使用系统默认值压缩能否节省空间取决于表列中的数据。
Whether or not space can be saved by using system default compression depends on the data in the table columns.
把XML数据分解为小片段并把它们映射到关系DBMS中的表列。
"Shredding" or decomposing the XML data into pieces and mapping these pieces to various columns of tables within a relational DBMS.
这个dataTable组件允许用户显示任何类型的表列数据，包括文本、图像、组合逻辑单元等等。
The dataTable component allows the user to display tabular data of any type, including text, images, combination (combo) boxes and more.
Name旁边提供的Select按钮将显示数据库对象的列表，在本例中为来自所选数据库的表列表。
The select button provided beside Name will show a list of database objects, in this case a list of tables from the selected database.
比方说你的数据库模式中包含三个作为主键的表列，分别命名为“list _ id ”、“ListId ”和“ list _ value ”。
Say your database schema contains three primary key table columns named "list_id", "ListId", and "list_value".
使用geninput脚本生成要从源数据库转移到DB2的表列表。
Use geninput script to generate a list of tables to be moved from source to DB2.
xsltransform函数可以将作为XML文档存储在数据库表列中的xslt样式表应用到XML文档上。
The xsltransform function can apply the XSLT stylesheet stored in a database table column as an XML document on an XML document.
以谱图表列数据或曲面拟合参数为放电样本的特征量。
The tabulated data or surface fitting parameters of these patterns were used as characteristic vectors.
显然是在命名数据库表列的问题。
IndexOutOfRangeException: No column with the specified name was found.
下表列出了将控件绑定到数据所需执行的一些最常见任务。
After you create controls that are bound to data, you might want to do one of the following tasks.
识别结果表明矩特征和三维谱图表列数据有较好的识别效果；
The results show that moment features and tabulated data are more effective than statistics and fractal parameters in distinguishing five kinds of discharge.
下表列出了元数据标记类型、每个标记类型表示的抽象以及包含该抽象元数据的元数据表的名称。
The following table lists the metadata token types, the abstraction that each token type represents, and the name of the metadata table that contains the abstraction's metadata.
下面的图表列出了此时间间隔内运行的所有进程以及每个进程的总计的统计数据。
The chart below that lists all the processes that ran during the interval, with the statistics accumulated for each process.
我们设计透视表组件的目的之一，就是使表列数据源和多维数据源的用户界面和编程模型保持一致。
One of our goals while designing the PivotTable component was to make the user interface and programming model consistent between tabular and multidimensional data sources.
数据透视表列表“%1”含有不完整的或已遭破坏的信息。
The PivotTable list "% 1" contains incomplete or damaged information.
我会解释每一个术语代表控件中的什么内容，以及这个术语对应表列数据源和多维数据源中的什么术语。
I will explain what each of these terms represents in the control and what the term maps to in both the tabular and multidimensional data source terminology.
我会解释每一个术语代表控件中的什么内容，以及这个术语对应表列数据源和多维数据源中的什么术语。
I will explain what each of these terms represents in the control and what the term maps to in both the tabular and multidimensional data source terminology.
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