微数据是向页面添加结构化数据的一种简便方式。
DB 2文本搜索能够对存储在DB 2数据库中的结构化和非结构化数据进行全文搜索。
DB2 text search enables full-text search on structured and unstructured data stored in a DB2 database.
这种以业务对象的形式传递数据的数据检索方法称为非传递或数据转换框架(DTF)，它仅适用于结构化数据。
This method of retrieving data, that is, passing data in the form of a business object, is called non-pass-through or data Transformation Framework (DTF), and it operates on structured data.
更多的结构化和非结构化数据可以比尔恩门所著的《参考数据库之父》的摘要。
For more on structured versus unstructured data read a synopsis by the father of data warehousing, Bill Inmon.
本示例中的数据属于结构化数据。
InfoSphereMashupHub客户机提供了用于从结构化和半结构化的数据源创建数据提要的工具。
InfoSphere MashupHub client provides tools to create data feeds from structured and semi-structured data sources.
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.
数据之间的关系中存在结构，但就该数据的意义而言它不是结构化数据。
There is structure in the relationships of the data, but the data is not structured with regard to the meaning of that data.
但是，此功能只适用于通过场景中的非结构化数据，因为这些数据不会转换为业务对象。
However, use this feature with unstructured data in a pass-though scenario because it is not going to be put into business objects.
我所谓的强大是指，那些解决方案必须能够从结构化数据(例如数据库和网页)和非结构化数据(例如文本、音频和视频)中提取可操作的信息。
By strong, I mean they must be able to extract actionable information from both structured data, such as databases and Web pages, and unstructured data, such as text, audio, and video.
此外，通过管道和容器，您可以以更加结构化的方式传递数据，并使得数据的转换更加简单容易。
Additionally, with channels and containers, you pass data in a more structured way, and it makes the data conversion simpler and easier.
尽管这个应用程序非常简单，但是它说明了使用UIMA在非结构化数据和结构化数据之间建立联系的主要特性。
While this application is very simple, it illustrates the main features of using UIMA to bridge between the worlds of unstructured and structured data.
在本文中，我们重点关注于结构化数据的数据整合模式。
In this article, we have focused on the data consolidation pattern for structured data.
我猜想处理XML文档的所有程序员中有80%真正需要的只是一种提取数据并将它们作为结构化的数据容易地进行操作的方法。
I would guess that what 80% of all the programmers who deal with XML documents really want is just a way to grab the data and easily manipulate it as structured data.
根据这里的描述，HTML看起来像结构化数据，但不是将隐藏语义应用到数据的结构类型。
The HTML looks structured as described here, but not the type of structure that applies the latent meaning to the data.
数据对象是通用的，它们提供了DMS创建的结构化数据的公共视图。
Data objects are generic and provide a common view of structured data built by a DMS.
例如，用户可以提取与一个客户相关的所有数据，即使是具有不同模式的结构化的数据。
For example, all the data relating to a single customer can be extracted, even though the data was structured with different schemas.
当这样的数据格式化成HTML，它就非常困难恢复成原始的结构化数据。
When this data is formatted into HTML, it becomes very difficult to recover the original structured data.
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.
将任意的结构化数据存储到关系数据库总是麻烦的。
Storing arbitrary structured data in a relational database will always be tricky.
然后，可以根据时效和访问策略，通过人工手动或自动化数据转移程序，将数据——主要是非结构化的数据——转移至云存储中。
Data — mostly unstructured — could then be relegated to the cloud, either manually or with an automated data mover based on aging and access policies.
数据对象是结构化数据的SDO表示。
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.
S3主要适用于文件存储，虽然它为图片和CSS样式表的低成本存储提供了一个不错的选择，但在存储关系型数据和结构化数据上它不是一个好的选择。
S3 is primarily geared towards file storage so although it makes a good choice for cheaply hosting images and CSS stylesheets, it's a not a good choice for storing relational or structured data.
与HTML使用标签来描述外观和数据不同，XML严格地定义可移植的结构化数据。
Unlike HTML, which uses tags for describing presentation and data, XML is strictly for the definition of portable structured data.
还记得吗，适配器创建的接口既具有用于读取非结构化数据的通用操作，又包含用于读取结构化数据的特定于业务对象的操作。
Recall that the adapter creates an interface that has a generic operation for reading unstructured data and an operation specific to the business object for reading structured data.
常规数据库可以存储高度结构化的数据和非结构化数据。
Regular databases can store both highly structured data and unstructured documents.
随着非结构化数据的爆炸性增长，要求提供能够同时对关系、非关系、结构化、非结构化数据源进行分析的技术。
With the explosive growth of unstructured data, technology is required to provide analytics on relational, non-relational, structured, and unstructured data sources.
实现结构化数据提升了数据的价值。
它提供一个用于存储结构化数据的数据存储api。
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