首先,清理数据和构建存储库几乎总是按每个项目逐一进行。
First, data cleansing and repository building are almost always carried out on a project by project basis.
在这种情况下,需要确保不必清理数据,或者确实可获得查找的数据。
In this case, you need to be sure you don't have to clean up data, or that you are actually getting the data you are looking for.
我们正在清理数据,我们发现名称列满是类似的名字代表同一公司。
We are cleaning up the data and we have discovered the name column is full of similar names that represent the same company.
通过清理数据并忽略被错误提交的数据,已经为保护应用程序奠定了良好的基础。
By cleaning up your data and ignoring data submitted improperly, you have made excellent first steps in securing your application.
Drupal 5.0现在实现了卸载模块的方法,从而能够清理数据库。
Drupal 5.0 now implements methods to uninstall modules so that the database can be cleaned up.
MGE包含着建立维持拓补清理数据的工具,而不用在建立维持拓补前进行处理和存储。
MGE contains tools for building and maintaining topologically clean data without the processing and storage overhead of building and maintaining topology.
下面的代码片段检查' postmsg '响应,向数据库添加数据,以及清理数据库。
Here's the snippet that checks for the 'postmsg' action, inserts the message into the database, and cleans it out on the fly as well.
正如您可能想到的,设计是数据清理流程中最重要和最复杂的阶段。
As you might imagine, design is the most critical and complex phase in the data cleansing process.
潜在收益和客户体验是通过将数据清理作为服务部署来实现的。
The potential revenue benefits and customer experience are realized by deploying data cleansing as a service.
数据清理的soa上下文允许对各个请求字符串进行标准化和匹配。
The SOA context for data cleansing allows for standardization and matching of individual request strings.
从此上下文中来看,数据清理模式允许企业将其验证与匹配功能扩展到创建点。
Viewed in this context, the data cleansing pattern allows an enterprise to extend its capabilities for validation and matching to the point of creation.
设计数据验证和清理。
开发成本主要取决于数据清理任务的复杂性。
The development costs depend largely on the complexity of the data cleansing task.
图1显示了在传统上下文中应用数据清理模式的抽象体系结构。
Figure 1 illustrates the high-level architecture of applying the data cleansing pattern in the traditional context.
它们还可提供各种信息处理功能,如通过分析和评分算法、数据清理规则等进行处理。
They also surface information processing capabilities such as the results of analytical and scoring algorithms, data cleansing rules, etc.
转换需求越复杂,变化越多,运行时转换或数据清理服务器一定就越复杂。
The more complex and varied the transformation requirements, the more sophisticated the run-time transformation or the data cleansing server must be.
数据清理模式的重要方面是,其注重企业级的可重用性。
An important aspect of the data cleansing pattern is its focus on reusability at an enterprise level.
应用数据清理模式时,务必了解其如何影响以下非功能需求。
When applying the data cleansing pattern, it is important to understand how it impacts the following nonfunctional requirements.
此服务器然后能以批处理模式处理和清理海量数据,并能在实时调用环境中以一次处理一个记录的方式进行实时处理。
This server is then able to process and cleanse extremely large data volumes in batch mode as well as single records in a real-time invocation environment.
清单1显示了进行一些清理和数据清除之后的一些示例。
Listing 1 shows some samples after some cleanup and data scrubbing.
务必注意,数据清理模式经常与其他模式一起应用;图3中绿色框就是这样的例子。
It is important to note that the data cleansing pattern is often applied together with other patterns; the green boxes in Figure 3 are such an example.
很多数据清理模式的实现都提供了成熟的工具来开发、测试和部署清理规则。
Many implementations of the data cleansing pattern provide sophisticated tools to develop, test, and deploy the cleansing rules.
数据清理模式的转换能力是专用的,重点是通过数据的标准化和匹配提高数据质量和完整性。
The transformation capabilities of the data cleansing pattern are specialized and focus upon improving data quality and integrity by standardizing and matching data.
简单描述了此方法后,我们将了解应用数据清理模式的上下文。
After briefly describing the value of this approach, you'll learn the context in which the data cleansing pattern should be applied.
无论计算机提供的数据清理算法构造如何巧妙,也只能解决数据问题当中非常小的一部分。
A computer algorithm for data cleansing, no matter how cleverly constructed, can only address a very small subset of data problems.
由于这些转换规则可能数量很多而且复杂,很多数据清理模式实现都使用数据清理服务器将清理规则作为转换操作部署。
Since these transformation rules may be numerous and complex, many implementations of the data cleansing pattern deploy the cleansing rules as transformation operations using a data cleansing server.
数据清理模式指定有关如何在输入时或稍后提高持久性数据的数据质量的建议性实践。
The data cleansing pattern specifies a recommended practice for how to improve the data quality of persistent data either at entry or later.
数据清理服务接受数据质量为未确定的数据作为输入。
The data cleansing service receives data with an undetermined level of data quality as input.
数据清理模式的传统上下文是数据库层,经常将数据清理模式应用到此层。
The traditional context of the data cleansing pattern is the database layer, which is where it is most often applied.
对于指定清理规则的开发人员或设计人员,有必要对要应用数据清理模式的数据源有足够的了解。
For the developer or designer to specify cleansing rules, a sufficient understanding of the data sources against which the data cleansing pattern shall be applied is necessary.
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