从全局上下文中清理数据。
清理数据是个大问题。
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.
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.
如果不能，谁将负责进行数据清理？
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.
正确地清理客户数据需要工作人员非常了解每个客户的情况。
Properly cleaning up customer data requires the knowledge of each individual customer.
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.
已经清理过的数据又开始走样了。
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.
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.
数据清理模式的产品实现对输入数据支持的格式有很大变化。
Product implementations of the data cleansing pattern vary in the range of formats they can support for input data.
服务将随后对源数据应用清理规则。
The service then applies the cleansing rules against the source data.
在很多情况下，数据清理模式与数据整合模式一起应用。
In many cases, the data cleansing pattern is applied together with the data consolidation pattern.
因此，数据清理服务器需要能够进行扩展，以处理大量数据。
Therefore, the data cleansing server needs to be able to scale in order to process large data volumes.
在保存信息前应用数据清理，可在输入点(如数据输入门户)将业务定义的验证机制包含进来。
Applying data cleansing before the information is persisted allows for the incorporation of business-defined validation mechanisms at the point of entry, such as in data entry portals.
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