• 第一个问题就是索引应用数据。

    The first problem is indexing the application data.

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  • 不应该应用数据联合模式的领域。

    Risk areas to apply the data federation pattern.

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  • 应用数据整合模式的危险领域。

    Risk area to apply the data consolidation pattern.

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  • 不应该应用数据清理模式的领域。

    Risk areas to apply the data cleansing pattern.

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  • 应用数据整合模式的主要领域。

    Focus areas to apply the data consolidation pattern.

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  • 应用数据联合模式时的约束。

    Constraints when applying data federation pattern.

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  • 通过可选的界面访问企业应用数据和功能。

    Access to enterprise app data and functionality via alternative interfaces.

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  • 应用数据消息可以是任何信息,这点很重要。

    It's important to realize that application data records can be anything.

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  • 应用数据整合模式的传统的、最典型场景包括

    The most typical scenarios in which the data consolidation pattern has traditionally been applied are

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  • 应用数据备份api——可以将应用数据备份到云中。

    Application data backup API - allows backing up application data to the cloud.

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  • 我们看到它的响应消息,包括泄露秘密的应用数据头。

    We see its response, including the tell-tale application data header.

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  • 可以在Android商店搜索应用,包括应用数据。

    Search through Android Marketplace applications including application data.

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  • 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.

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  • 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.

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  • 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.

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  • 你可以复制照片和音乐,而开发者可以复制缓存和其他应用数据。

    You can copy photos and music on there while developers can copy caches and other application data.

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  • 对大量数据应用数据清理模式与对各个记录应用此模式一样常见。

    It is fairly common to apply the data cleansing pattern against sets of bulk data as well as individual records.

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  • 在应用数据整合模式时,了解它对下列非功能性需求的影响是非常重要的。

    When applying the data consolidation pattern it is important to understand how it impacts the following nonfunctional requirements.

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  • 另一个原因是美国公司对于收集、应用数据来预测消费者需求还不是很内行。

    Another reason is that American firms are less adept at capturing and using customer data to predict demand.

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  • “数据可视化”这个词经常用来描述应用数据的图形化视图,如图表和图形。

    Data visualization is a term frequently used to describe graphical views of application data, such as charts and graphs.

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  • 目标服务器在应用数据之前,将行数据从主字典格式转换为本地表字典格式。

    The target server converts the row data from the master dictionary format to the local table dictionary format before applying the data.

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  • 在以下部分中,以星期视图为例,演示如何将XML应用数据变换成HTML。

    In the following, the week view is used as an example of how to transform XML application data to HTML.

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  • 描述了应用数据联合的上下文后,我们将讨论此模式所针对的问题及其解决方案。

    After describing the context in which we apply data federation, we discuss the problem that this pattern addresses, as well as the solution.

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  • Okta和Snaplogic帮助企业将他们的身份和应用数据分别进行互联。

    Okta and Snaplogic help businesses connect their identities and application data together respectively.

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  • 我们最后将总结何时应用数据联合模式及何时不应用此模式,并将列出重要的约束。

    We will conclude by summarizing when to apply and when not to apply the data federation pattern and to list important constraints.

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  • 应用数据联合模式时,务必理解其特征以及其受以下所述的非功能要求的影响情况。

    When applying the data federation pattern, it is important to understand its characteristics and how it is affected by the non-functional requirements described below.

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  • 在传统上下文中应用数据联合模式的解决方案可以利用SQL的声明性和灵活性特征。

    Solutions that apply the data federation pattern in the traditional context leverage the advantage of the declarative and flexible nature of SQL.

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  • 这种恢复并不会导致系统或应用数据的丢失,也不会丢失设置,甚至连缓存都不会丢失。

    This recovery is done without losing system or application data, without losing Settings and even without losing the cache.

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  • 在soa上下文中应用数据联合模式时,需要在SOA内将一组联合查询作为服务启用并注册。

    When applying the data federation pattern in an SOA context, a set of federated queries need to be enabled and registered as services within SOA.

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  • 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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