该方法不仅可灵活、准确地完成数据采集，还同时完成了数据的清理工作。
This method not only finishs data acquisition agilely and rightly, but also finishs data cleaning in the same.
如果这个队列满了，就不允许应用程序发送更多数据，直到清理完积压的队列为止。
If this queue is full, the application is not allowed to send any more data until the backlog is cleaned up.
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.
而缺点在于，它是日志结构的，所以需要垃圾收集，以便清理旧的数据和元数据。
The downside is that it's log structured, and garbage collection is needed to clean up old data and metadata.
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.
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.
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.
已经清理过的数据又开始走样了。
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.
公布数据是清理大规模油污的第一步。
The data generated is the first step towards a massive clean-up.
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.
他们可以利用GPS定位数据协同工作，策划出最有效的清理方案。
They coordinate with each other by using GPS location data. That lets them plot out the most efficient way to tackle a clean-up project.
数据清理模式的价值主张。
不应该应用数据清理模式的领域。
除此之外，Cake在Sanitize组件中提供了许多方法可以清理您的数据。
Beyond that, Cake provides a number of methods in the Sanitize component you can use to clean up your data.
Cake的sanitize类使得清理您的数据变得很容易。
数据清理模式中的可重用性是通过清理规则的定义来实现的，这些清理规则可以在记录级别应用，也可以在服务或通过大量数据的批处理应用。
Reusability in the data cleansing patterns is realized through the definition of cleansing rules that can be applied at the record level through a service or through a batch process for bulk data.
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.
其目的是评估服务实现在数据质量方面是否将满足要求的服务级别，以及是否需要附加的数据转换或数据清理操作。
The objective is to assess if service implementations will meet the required service levels regarding data quality and if additional data transformations or data cleansing operations are required.
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.
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.
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.
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.
新的清理服务使您可以计划数据库中已完成实例的删除，从而带来与清理服务器不相上下的功能。
A new cleanup service enables you to schedule deletion of completed instances from the database, bringing functional parity with the cleanup server.
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.
在数据世界里，数据管理的惯用方法基本上是定期清理。
In the data world, the traditional approach to data management is largely about periodic cleaning.
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.
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.
管理错误也可能导致意外删除重要的文件，比如在清理旧日志时，意外地删除了数据库的活动事务日志。
Administrative error can also cause important files to be inadvertently deleted, such as accidentally removing the active transaction log of a database while cleaning up the old logs.
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