信息可以为结构化和非结构化数据。
结构化和非结构化数据。
同时，XML也能够描述结构化和非结构化数据。
At the same time, XML is also capable of describing structured and unstructured data.
在这两个场景中，非结构化数据的主要类型是文本。
In both of the above scenarios, text is the main type of unstructured data.
添加来自非结构化数据的信息可以显著增强这些预测模型。
Adding information from unstructured data could enhance these predictive models significantly.
与非结构化数据不同，可以使用描述性标签对元素进行分类。
Unlike unstructured documents, the individual elements can be classified using descriptive labels.
不管使用什么技术，索引大量非结构化数据是一件很艰难的任务。
Indexing large amounts of unstructured data is a difficult task regardless of the technologies involved.
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.
参与垂直搜索项目与海量结构化、非结构化数据的处理。
Work for the design and implementation of vertical search engine as well as structured/unstructured data processing.
另外，如果结构化、半结构化和非结构化数据的数量大体相当，那么怎么办呢？
And what do you do if the amount of structured, semi-structured, and unstructured data is roughly equal?
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.
非结构化数据与结构化数据的融合是数字矿山建设的重要组成部分。
Data Fusion of the non-structured data and structured data plays an important role in the construction of Digital Mine.
不同分析产品的一些价值在于它们处理大量非结构化数据以发现隐藏含义的能力。
Some of the value of different analytics products is their ability to process large amounts of unstructured data to discover the latent meaning.
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.
由于云和Hadoop的出现，及时处理大量的结构化或非结构化数据目前已成为可能。
Thanks to the cloud and Hadoop, it is now possible to handle large amounts of structured or unstructured data in a timely manner.
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.
图6显示了如何使用Hadoop来分析数据，以提供关于结构化和非结构化数据的分析。
Figure 6 shows Hadoop used for historical data analysis to provide analytics on structured and unstructured 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.
由于非结构化数据本身的数据特点，使得它与结构化数据的融合面临极大的困难。
Data fusion is confronted with heavy difficulty because of the characteristics of the non-structured data.
它取代了2004开始试探的最初索引算法，它已经证明在处理大量和非结构化数据集时更有效。
It replaced the original indexing algorithms and heuristics in 2004, given its proven efficiency in processing very large, unstructured datasets.
此产品还可以与以下两个产品组合使用，以聚合结构化、非结构化数据以及来自大型机平台的宝贵资产。
This product can also be combined with the following two products to aggregate structured, unstructured data and the valuable assets from the mainframe platforms.
非结构化数据可以改进现有的BI分析的质量，在某些情况下还有助于实现新的信息探查类型。
Unstructured data can improve the quality of existing BI analytics, or in some cases, it can be the key enabler for new types of insight.
Hadoop是一个Apache软件基金会项目，包含一系列用于存储和处理大量非结构化数据的工具集。
Hadoop is an Apache Software Foundation project that consists of a set of tools for storing and processing large amounts of unstructured data.
结论是，电力工程管理数据挖掘必须有一定的数据体系支持，对非结构化数据信息的整理是关键。
The conclusion is that the data mining of power project management should be supported by a certain data system and what how to coordinate the unstructured data information would be the key.
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.
论文通过对专家所发表的论文，承担的课题等非结构化数据进行挖掘，来自动判别出专家的知识领域。
In the paper, unstructured data, such as papers published by experts and subjects committed by experts, is mined to recognise the knowledge domain of experts automatically.
由于视频、音频、图形和web应用程序生成天文数量的数据，非结构化数据的数量正在而且会继续呈指数增加。
The amount of unstructured data is and will continue to increase exponentially due to astronomical data generated from videos, audios, graphics and web applications.
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.
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.
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