外文文献翻译 - 基于 Web 的分析系统

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2.3 使用 Web 瘦客户端访问多维数据

开发小组成员最后选用 Microsoft SQL Server Resource Kit，为我们的难题找到了解决方案：分析服务 Web 瘦客户端浏览器。（要在资源工具箱 CD-ROM 上访问 Web 瘦客户端，参见资源工具箱第 39 章的参考信息，该信息位于 http://www.microsoft.com/technet/treeview/default.asp?url=/technet/prodtechnol/sql/reskit/sql2000/part11/c3961.asp。）Web 瘦客户端使用 Active Server Page (ASP) 连接到分析服务器、将多维数据转换成 HTML，并将数据传递给客户端。说明了 Web 瘦客户端显示贷款数据子集的情况。

Web 瘦客户端需要 IE 5.0 或以上版本。因为客户端不直接连接到分析服务计算机，所以客户端不需要 MDAC 2.6。由于多数客户端订户具有 IE 5.0，所以我们不必在订户的个人计算机上部署 Service Pack。图 5显示了 Web 瘦客户端使用的结构。Web 瘦客户端使用 ASP 从 Microsoft IIS 服务器 — 而不是客户端 — 来查询多维数据集。Web 瘦客户端带有 ASP 页，使得您能够在 IIS 服务器上部署。因为只有运行 ASP 的 IIS 服务器才能够连接到分析服务数据库，所以，您能够使用一个连接来保证通过防火墙进行的多维数据集访问，并且可以将该连接局限于 Web 服务器和数据库服务器。这种安排创建了一个高效和易于保护的连接。对客户端的唯一连接是标准的 HTML 连接，这能够减少防火墙的影响。

Web 瘦客户端将查询的数据放到一个网格状的 HTML 表结构中，并将数据发送到浏览器。用户通过 JavaScript 和数据进行交互。通过使用透明层，用户可以将维拖到多维数据集中进行数据操作、通过维向下追溯并显示和分析其需要的数据。

如多数设计决策那样，使用 Web 瘦客户端需要做出某些折衷。因为在 Web 服务器域和后端域之间不存在信任关系。您不能在系统中扩展 Active Directory (AD) 作为其验证服务。这样的信任关系会给入侵者提供一种破坏后端域安全的方法。因此，系统必须建立一个匿名用户来访问分析服务器上的数据。如果对数据的访问取决于用户，则不能使用基于分析服务的角色安全性来控制对数据的访问。然而，如果系统允许所有用户通过一个安全上下文进行数据访问，则使用 Web 瘦客户端是安全的，而且容易部署。

Web 瘦客户端也有其他缺点。OWC 通过使用数据透视表的智能缓存把从 Web 浏览器到 OLAP Web 服务器的往返行程数减少到最小来获取更多的数据。但 Web 瘦客户端是从服务器端提取数据的，因为数据在 Web 浏览器中并不缓存，所以每次对数据

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的更改都要求浏览器向 Web 服务器提出新的请求。当处理的数据量很大时，这个进程会很缓慢。OWC 还具有丰富的对象模型，您可以对之编写自定义代码。而 Web 瘦客户端使用 JavaScript，很难自定义。因为 Web 瘦客户端是资源工具箱内包含的“自由代码”，所以 Microsoft 并不像支持 Excel 或 OWC 那样对 Web 瘦客户端提供相同程度的支持。而且，Web 瘦客户端要求客户端脚本，如果 Web 浏览器已经更新或更改，它会产生错误。此外，Web 瘦客户端不直接支持对服务器的回写。幸好，我们小组的应用程序不要求回写， 所以，这个缺失的功能并不妨碍我们使用 Web 瘦客户端。在对其优点和缺点进行评估后，我们决定部署 Web 瘦客户端。这个解决方案在生产环境中使用效果不错，并得到了用户的广泛认可。 3 为任务选择合适的客户端

我们的开发小组在本项目中得到的经验教训能够为在企业中部署 OLAP 解决方案提供有价值的指导。Excel 提供了功能强大而且熟悉的客户端，您可以在局域网中进行部署，但需要实时连接 OLAP 服务器。在 Intranet 内部署分析服务客户端时，OWC 的使用效果不错，因为您可以轻松控制客户端平台，并且能够在 Intranet 内安全地打开端口。当存在防火墙，并且您希望尽可能减少对用户操作系统的影响时，Web 瘦客户端是一个很好的 Internet 解决方案。对于任何开发项目，需要理解所开发产品的使用者的业务要求和需要。通过概括要求并权衡所有选择，可以发现能够满足客户要求的正确解决方案。

摘自：马克斯科特，约翰琳.基于 Web 的分析系统[A].微软官方网站,2007.2

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附：英文原文

Overview of Jscript8.0 Arrays

James street

1 Summary

When working with analytical databases, analysts organize data into common groups and try to determine what would happen if things were different.

For example, would increasing a product's price?which would increase profit per unit but probably reduce number of units sold?yield a higher or lower overall profit? Or how would a drop in the federal discount rate affect the yield of real estate loans? To help analysts make educated projections based on historical trends, Microsoft provides Analysis Services in SQL Server 2000 and OLAP Services in SQL Server 7.0. These services provide OLAP capability and can process data stored in SQL Server (or any other OLE DB?compatible data source) into multidimensional data structures called cubes. Data cubes simplify the process of analyzing trends and correlating the way entities interact with one another.

For example, real estate investors use cash-flow modeling to isolate a group of loans that have common characteristics (e.g., types of properties, geographic area, range of interest rates) and project the effects of different kinds of events. What will happen if loans mature more rapidly than expected or if the borrowers default? And how might such unpredictable events affect the yield of bonds that the loans secure?

Selecting from lists that can include hundreds of loans and isolating the loans that have the characteristic that you're analyzing can be tricky. Analysis Services and OLAP Services can help correlate these groups of loans so that analysts can model loan assumptions. To help a client's real estate analysts project the performance of commercial mortgage-backed securities, our development team needed to devise a system that simplified the grouping of

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loans in different ways?such as by their interest rate, term to maturity, or property location. The interface needed to be easy to learn and use. And the system we developed needed to be securely deployed through the Internet. To meet these criteria, the development team chose Analysis Services. 2 Deploy Office OWC

Having settled on a back-end technology, the development team began working on a plan for implementing the front-end interface. Most financial analysts use Microsoft Excel and are familiar and comfortable with its interface. Excel includes PivotTable Service, which lets analysts connect to Analysis Services databases. Excel's drag-and-drop interface provides simple, intuitive access to multidimensional data without requiring users to have extensive training. And by using Excel's graphing capabilities, users can present data in graphs and charts. So for the front-end interface, the team's first choice was Excel 2002, which is part of Microsoft Office XP. Figure 1 shows Excel's PivotTable Service exploring an Analysis Services OLAP cube. 2.1 Deploy Office OWC

Excel would have been a fine choice?if all the client's users worked together in the same building and could access the Analysis server through the same LAN. But because the users needed to share the application from a variety of organizations whose offices are scattered around the world, the team needed a component similar to Excel that users could access through the Internet. The team found the solution to this challenge in Office Web Components. OWC is a set of ActiveX controls that you can use on Web pages to provide Office functionality. The OWC PivotTable component is a Web version of Excel's PivotTable Service; PivotTable uses PivotTable Service and requires that PivotTable Service be installed before it will run. But the OWC PivotTable works without Excel.

PivotTable can retrieve multidimensional data from an Analysis server and present the data in an interactive, drag-and-drop interface. Users who have Microsoft Internet Explorer (IE) 4.01 or later can use OWC to analyze Analysis Services data without installing additional component software. Figure 2 shows the OWC PivotTable client interface, which looks and