使用udi计数器跟踪数据变化。
我可以给出图表中数据变化的原因吗？
Can I suggest reasons for changes shown on graphs or charts?
非线性数据变化较大。
以水准点高程数据变化说明地面沉降历史现状。
Historical actuality of ground settlement is explained Using changes of benchmark height data.
Catalyst称，这些细微的数据变化在统计学上意义不大。
The slight changes in the Numbers are not considered statistically significant, Catalyst said.
迁移用包装的方式组合了协调模式变化和数据变化的威力和简单性。
Migrations combine power and simplicity to coordinate both schema changes and data changes using a wrapping approach.
事实上，正确的思路应该是：执行查找的频率与数据变化的频率有关。
In fact, the line of reasoning should simply be how frequently your data changes with respect to how frequently you perform your lookups.
过去十年里，有关全球气温增长以及未来海平面增长的数据变化很大。
Over the past decade there have been large variations in the projected increase in global temperatures and sea levels over the coming years.
有些数据库变化要求数据和模式一起变化，有些数据变化要求逻辑变化。
Some database changes require changes in data as well as schema, and some of those data changes require logical changes.
它允许您定义一个数据变化阈值，统计也只有数据变化阈值到达时才会更新。
It allows you to define a data change threshold, and statistics are refreshed only when the data change threshold is reached.
合并后，模板或者相关组件的数据变化不会自动在视图中反应出来。
After the merge occurs, changes to the model or related sections of the view are NOT automatically reflected in the view.
首先，我们展示如何保护JTable的UI代码不因任何数据变化而遭到更改。
First, we'd like to show how we were able to protect the JTable's UI code from being altered by any data changes.
变化监测对于识别数据模式中您用于未来分析和预言的数据变化是有益的。
Change detection can be useful for identifying changes in data patterns that you can use for future analysis and prediction.
但是，他认为死亡率纪录的降低大部分是由于一些人口不多的国家数据变化引起的。
However, he is troubled that much of the drop in mortality recorded is the result of changes in the figures for a mere half-dozen countries.
请不要期望任何会话bean方法的输出能反映因运行其它方法而造成的数据变化。
Do not expect the output of any of the session bean methods to reflect data changes from running other methods.
基于日志的数据变化。通过读取数据库中日志的变化来捕捉变化的数据。
Log-based data capture reads data changes from proprietary database recovery logs.
可以同步数据和模式中的变化，也可以解决涉及对模型对象进行逻辑操作的数据变化。
You can synchronize changes in data and schema. You can also address data changes that involve logical operations on your model objects.
对于单、双测点数据，研究相邻数据变化率的方法和曲线拟合残差的方法。
One is based on the rate of adjacent data change and the other is based on curve fitting residual.
在数据较少或变形数据变化较大时，组合模型预测值明显优于单一模型预测值。
Under the condition of small and unstable data, the combined prediction value is superior to the single grey model.
从数据挖掘查询返回的XML数据的消费者通常只对自上次查询以来发生的数据变化感兴趣。
Consumers of XML data returned from data-mining queries are often interested only in the data changes made since a previous query.
但由于产地不一，尺码会发生数据变化，希望客户们购买前和店铺沟通了解，避免发生纠纷。
However, due to different origin, size of data changes will happen, want customers to know before you buy and store to communicate to avoid disputes.
但是很少有团队会费力气创建能取消模式变化的脚本，而且成功地创建了处理数据变化的自动脚本的团队更少。
But very few teams bother to create scripts that undo schema changes, and fewer still manage to create automated scripts to deal with changes in data.
但用BEZIER曲线显示数据变化规律时，若控制点数量不足，就难以精确反映数据的变化规律。
But not having enough control points that have been given, we can't accurately show the regulation of data change with the BEZIER curve.
更多情况下，我们不知道什么时候数据变化了，尤其是在那些有很多种途径改动数据的程序中——可能在程序中很远的地方。
More often we don't know when the data have changed, especially in applications that mutate data in many ways, perhaps in application locations far away.
流数据具有大数据量、数据变化频繁、需要快速响应、查询次数有限的特点，这些特点使得流数据的处理需要采用新的方式。
Data stream's characteristic is that it has the huge size, its data is continual change, it needs response quickly and its times of query are limit.
但通过AIR，这些类型的框架现在可以通过连接进行发布和运行，它们同时也拥有一种强大的方式保证在断开连接后依然保存数据变化。
With AIR, these types of frameworks now have a means of being delivered and run with a connection, and a robust means to store data changes while disconnected.
在典型的数据仓库环境中，最新的数据变化频繁，而历史数据变化相对较小，这使得分区级别的REORG对于针对活动数据的重组尤为重要。
In typical warehouse environments, recent data changes frequently while historical data is more or less static and this makes partition level reorg invaluable for reorganizing only the active data.
在典型的数据仓库环境中，最新的数据变化频繁，而历史数据变化相对较小，这使得分区级别的REORG对于针对活动数据的重组尤为重要。
In typical warehouse environments, recent data changes frequently while historical data is more or less static and this makes partition level reorg invaluable for reorganizing only the active data.
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