Create data sets on the mainframe.
在大型机上创建数据集。
It is best used on small data sets.
所以，最好将它用于较小的数据集。
Other data sets need to be specified.
其他数据集需要指定。
The size of the active log data sets.
活动日志的数据集的大小。
Reading large data sets into memory.
将大型数据集读入内存中。
How many active log data sets are defined?
定义多少活动日志的数据集。
The input may contain several data sets.
输入数据可以包含若干数据集。
Now you can use the object in other data sets.
现在您可以使用其他数据集合中的对象。
Creating data sets to get work item information.
创建数据集以得到工作性信息。
This is the starting value for the two data sets.
这是两个数据集的起始值。
Create the recovery data sets used by II Classic.
创建IIClassic使用的恢复数据集。
Part 2 will cover complex reports and nested data sets.
第2篇涉及了复杂的报表与固定的数据集。
Also, the number of participating data sets is rapidly growing.
此外参加该计划的数据集还在不断增加。
The analysis used data sets on river stressors around the world.
该分析使用了世界各地河流压力的数据集。
How can I perform full outer joins of large data sets in r?
我怎么能执行完全外连接的大型数据集在R ？
Of course, things can get more complicated in real-life data sets.
当然，对于真实的数据集合，事情可能会变得更复杂。
But in a BIRT report, you can create as many data sets as you want.
但是在一个BIRT报表中，您可以创建尽可能多的数据集。
Finally, Pig is a platform on Hadoop for analyzing large data sets.
最后，Pig是Hadoop中用于分析大型数据集的平台。
Thus, the algorithm is fast even for extremely large data sets.
因此，甚至是对于超大数据集，算法也很快速。
One. The key to display the page with a page or data sets with.
用于显示页面加翻页、数据设置加。
Using these data sets, we can get the work items and their structure.
使用这些数据集，我们可以得到工作项目和它们的结构。
Visualization of large data sets is a complicated and demanding task.
大型数据集的可视化是一个复杂而艰巨的任务。
Do test data sets for software qualification represent realistic data?
是否用于软件评价的测试数据组能代表真实数据？
Security and authorization to the data sets must be considered here as well.
还必须考虑数据集的安全性和授权。
Begin to create the data sets needed, unless you have previously created them.
开始创建所需要的数据集，除非您以前已经创建了它们。
If this is correct then how does one go about pre-labelling large data sets?
如果这是正确的那么如何去预标记的大型数据集？
The benefit of this approach is that it need not labeled training data sets.
该方法的优点是不需要标示或训练数据集。
In the meantime, let's continue our discussion of recursing of inductive data sets.
在此，我们继续讨论归纳数据集的递归。
This code formats the data and sets the values on the appointment form.
下面这段代码格式化数据，并设置约会表单上的值。
It definitely sets a standard for transparency in data.
它在数据透明度方面绝对是树立了一个标准。
应用推荐