通过大型数据集编写XML文档。
将大型数据集读入内存中。
我怎么能执行完全外连接的大型数据集在R ？
如果这是正确的那么如何去预标记的大型数据集？
If this is correct then how does one go about pre-labelling large data sets?
最后，Pig是Hadoop中用于分析大型数据集的平台。
Finally, Pig is a platform on Hadoop for analyzing large data sets.
如果是使用大型数据集，可考虑用专用的缓冲池来代替表空间。
If you are working with large data sets, consider using a dedicated buffer pool for the table space.
我们正努力给大家提供一种方式，通过大型数据集驱动测试场景。
We are trying to give people a way to drive their testing scenarios via a large data set. Say for example you've written the following test in Twist
在云环境中，MapReduce结构提高了大型数据集的吞吐效率。
In a cloud environment, the MapReduce structure increases the efficiency of throughput for large data sets.
初始向量空间的创建时间可能很长，尤其对于大型数据集来说更是如此。
Initial vector-space creation times can be long, especially for large data sets.
例如它可以用于气候变化状况的建模，或进行大型数据集分析。
It can be used to model climate change situations, for instance, or to perform analysis of large data sets.
您可以使用该控件显示小型到大型数据集的只读或可编辑视图。
You can use the control to show read-only or editable views of small to very large sets of data.
通过本文很容易看出Hadoop显著简化了处理大型数据集的分布式计算。
From this article, it's easy to see how Hadoop makes distributed computing simple for processing large datasets.
我们可以使用Perl快速操作来自文件或RDBMS的大型数据集。
You can use Perl to quickly manipulate large sets of data from files or RDBMSs.
收集个人无法读完的大型数据集并将它们提炼为有用的数据是一个很重要的目标。
To take datasets much larger than a single person can read and boil them down to useful data is a primary goal.
但是在编写XML文档时，通常要联接和重构大型数据集，以匹配所需的文档结构。
However, when composing XML documents, it is common that large datasets need to be joined and restructured to match the desired document structure.
Hadoop可用于许多应用程序上，其已超越了为大型数据集简单计算字数的工作。
Hadoop can be used in many applications beyond simply computing word counts of large data sets.
Google引用MapReduce的概念作为处理或生成大型数据集的编程模型。
Google introduced the idea of MapReduce as a programming model for processing or generating large sets of data.
以XML数据库功能为中心的XQuery实现最适合高效地处理大型数据集。
XQuery implementations that focus on XML database functions are best used for handling large datasets efficiently.
神话：XQuery不能处理大型数据集，永远赶不上关系数据库的运行速度
Myth: XQuery will not scale to handle large datasets; XQuery will never be as fast as relational databases
复杂性不断提升的大型数据集已经将传统的数据挖掘技术推向新层次的处理要求。
Large data sets with escalating complexity have pushed traditional data mining techniques to new levels of processing demand.
通常，用户被迫抓取web或者挖掘社会媒体网站来建立他们自己的大型数据集。
Usually, users are forced to crawl the web or mine social-media sites to build their own.
这些字段适用于操作大型数据集，以帮助用户处理可视化、评估或实际解决重要问题。
These fields tend to manipulate large data sets to assist the user in visualizing, estimating, or actually solving non-trivial problems.
很少有人使用人工智能；通常，数据挖掘只是搜索并汇集大型数据集，以查找有用的信息。
Few people work with artificial intelligence; most commonly, data mining simply entails the ingesting of large data sets and searching through them to find information that is useful.
Xdmx的主要用途，是在专门研究大型数据集的可视化的大学和研究机构中，用作大型显示系统。
The primary usage of Xdmx is in large-scale display systems at universities and research institutions focusing on visualization of large datasets.
顾名思义，这是一个现成的ZendFramework组件，您可以用它来为大型数据集分页。
As the name suggests, this is a ready-made Zend Framework component that you can use to paginate large data sets.
使用number属性可以允许一个更干净的表示，尤其是大型数据集上的 repeat 的表示。
Use of the number attribute can permit a cleaner presentation, particularly of repeats over very large sets of data.
您可以向应用程序添加的另一项特性是搜索功能，该功能对于用户必须处理大型数据集时尤为有用。
Another feature you can add to an application is a search feature, which is especially useful if users must deal with large sets of data.
对于大型数据集来说，无论它们是文本还是数值，一般都可以将类似的项目自动组织，或集群，到一起。
Given large data sets, whether they are text or numeric, it is often useful to group together, or cluster, similar items automatically.
但是，在处理超大型数据集时，您还可以通过遵循以下一般原则进一步改善原生xml数据库的查询响应时间。
However, when dealing with very large data sets, you can further improve the query response times of a native XML database by following a few general common sense guidelines.
随着数据集的数据量和维数的增加，建立高效的、适用于大型数据集的分类法已成为数据挖掘的一个挑战性问题。
With the growth of data in volume and dimensionality, it has become a very challenging problem to build a high-efficient classifier for large databases.
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