【正文】 外 文 翻 譯 畢業(yè)設計 題目： 基于 web 的學生宿舍管理系統(tǒng) 宿舍管理與學生管理模塊 原文 1： Building a WebBased Analysis System Part1 譯文 1： 構建基于 Web 的分析系統(tǒng) 第一部分 原文 2： Building a WebBased Analysis System Part2 譯文 2： 構建 基于 Web 的分析系統(tǒng) 第二部分 原文 1 Building a WebBased Analysis System Part 1 A realworld look at using the Analysis Services Thin Web Client Browser Mark Scott, John Lynn Using OWC to Deploy Office on the Web When working with analytical databases, analysts anize data into mon groups and try to determine what would happen if things were different. For example, would increasing a product39。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 2020 and OLAP Services in SQL Server . These services provide OLAP capability and can process data stored in SQL Server (or any other OLE DB— patible 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 cashflow modeling to isolate a group of loans that have mon characteristics (., 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 you39。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 client39。s real estate analysts project the performance of mercial mortgagebacked securities, our development team needed to devise a system that simplified the grouping of 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 Inter. To meet these criteria, the development team chose Analysis Services. Having settled on a backend technology, the development team began working on a plan for implementing the frontend interface. Most financial analysts use Microsoft Excel and are familiar and fortable with its interface. Excel includes PivotTable Service, which lets analysts connect to Analysis Services databases. Excel39。s draganddrop interface provides simple, intuitive access to mul tidimensional data without requiring users to have extensive training. And by using Excel39。s graphing capabilities, users can present data in graphs and charts. So for the frontend interface, the team39。s first choice was Excel 2020, which is part of Microsoft Office XP. Figure 1 shows Excel39。s PivotTable Service exploring an Analysis Services OLAP cube. Excel would have been a fine choice— if all the client39。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 anizations whose offices are scattered around the world, the team needed a ponent similar to Excel that users could access through the Inter. The team found the solution to this challenge in Offi