完整版)_毕业设计(论文)外文翻译_(原文

1、毕业设计（论文）外文翻译（原文）NEW APPLICATION OF DATABASERelational databases in use for over two decades. A large portion of the applications of relational databases in the commercial world, supporting such tasks as transaction processing for banks and stock exchanges, sales and reservations for a variety of bus

2、inesses, and inventory and payroll for almost of all companies. We study several new applications, which recent years.First. Decision-support systemAs the online availability of data , businesses to exploit the available data to make better decisions about increase sales. We can extract much informa

3、tion for decision support by using simple SQL queries. Recently support based on data analysis and data mining, or knowledge discovery, using data from a variety of sources.Database applications can be broadly classified into transaction processing and decision support. Transaction-processing system

4、s are widely used today, and companies generated by these systems.The term data mining refers loosely to finding relevant information, or “discovering knowledge,”from a large volume of data. Like knowledge discovery in artificial intelligence, data mining attempts to discover statistical rules and p

5、atterns automatically from data. However, data mining differs from machine learning in that it deals with large volumes of data, stored primarily on disk.Knowledge discovered from a database can be represented by a set of rules. We can discover rules from database using one of two models:In the firs

6、t model, the user is involved directly in the process of knowledge discovery.In the second model, the system is responsible for automatically discovering knowledgefrom the database, by detecting patterns and correlations in the data.Work on automatic discovery of rules influenced strongly by work in

7、 the artificial-intelligence community on machine learning. The main differences lie in the volume of data databases, and in the need to access disk. Specialized data-mining algorithms developed to which rules are discovered depends on the class of data-mining application. We illustrate rule discove

8、ry using two application classes:classification and associations.Second. Spatial and Geographic DatabasesSpatial databases store information related to spatial locations, and provide support for efficient querying and indexing based on spatial locations. Two types of spatial databases are particular

9、ly important:Design databases, or computer-aided-design (CAD) databases, are spatial databases used to store design information about databases are integrated-circuit and electronic-device layouts.Geographic databases are spatial databases used to store geographic information, such as maps. Geograph

10、ic databases are often called geographic information systems.Geographic data are spatial in nature, but differ from design data in certain ways. Maps and satellite images are typical examples of geographic data. Maps may provide not only location information -such as boundaries, rivers and roads-but

11、 also much more detailed information associated with locations, such as elevation, soil type, land usage, and annual rainfall.Geographic data can be categorized into two types: raster data (such data consist a bit maps or pixel maps, in two or more dimensions.), vector data (vector data are construc

12、ted from basic geographic objects). Map data are often represented in vectorformat.Third. Multimedia DatabasesRecently, there much interest in databases that store multimedia data, such as images, audio, and video. Today multimedia data typically are stored outside the database, in files systems. Wh

13、en the number of multimedia objects is relatively small, features provided by databases are usually not important. Database functionality becomes important when the number of multimedia objects stored is large. Issues such as transactional updates, querying facilities, and indexing then become impor

14、tant. Multimedia objects often they were created, who created them, and to what category they belong. One approach to building a database for such multimedia objects is to use database for storing the descriptive attributes, and for keeping track of the files in which the multimedia objects are stor

15、ed.However, storing multimedia outside the database makes it the basis of actual multimedia data content. It can also lead to inconsistencies, such a file that is noted in the database, but whose contents are missing, or vice versa. It is therefore desirable to store the data themselves in the datab

16、ase.Forth. Mobility and Personal DatabasesLarge-scale commercial databases stored in central computing facilities. In the case of distributed database applications, there strong central database and network administration. Two technology trends which this assumption of central control and administra

17、tion is not entirely correct:1. The increasingly widespread use of personal computers, and, more important, of laptop or “notebook”computers.2. The development of a relatively low-cost wireless digital communication infrastructure, base on wireless local-area networks, cellular digital packet networ

18、ks, and other technologies.Wireless computing creates a situation where machines no longer at which to materialize the result of a query. In some cases, the location of the user is a parameter of the query. A example is a traveler s information system that provides data on the current route must be

19、processed based on knowledge of the user s location, direction of motion, and speed.Energy （battery power） is a scarce resource for mobile computers. This limitation influences many aspects of system design. Among the more interesting consequences of the need for energy efficiency is the use of sche

20、duled data broadcasts to reduce the need for mobile system to transmit queries. Increasingly amounts of data may reside on machines administered by users, rather than by database administrators. Furthermore, these machines may, at times, be disconnected from the network.SummaryDecision-support syste

21、ms are gaining importance, as companies realize the value of the on-line data collected by their on-line transaction-processing systems. Proposed extensions to SQL, such as the cube operation, of summary data. Data mining seeks to discover knowledge automatically, in the form of statistical rules an

22、d patterns from large databases. Data visualization systems data as well as geographic data. Design data are stored primarily as vector data; geographic data consist of a combination of vector and raster data.Multimedia databases are growing in importance. Issues such as similarity-based retrieval a

23、nd delivery of data at guaranteed rates are topics of current research.Mobile computing systems , leading to interest in database systems that can run on such systems. Query processing in such systems may involve lookups on server database. 毕业设计（论文）外文翻译（译文）数据库的新应用我们使用关系数据库已经有 20 多年了，关系数据库应用中有很大一部分都用

24、于商业领 域支持诸如银行和证券交易所的事务处理、各种业务的销售和预约，以及几乎所有公司都 需要的财产目录和工资单管理。下面我们要研究几个新的应用，近年来它们变得越来越重 要。1、决策支持系统 由于越来越多的数据可联机获得，企业已开始利用这些可获得的数据来对自己的行动 做出更好的决策，比如进什么货，以及如何最好的吸引顾客以提高销售额。我们可以通过 使用简单的 SQL 查询语句提供大量用于决策支持的信息。 但是，人们最近感到需要使用多 种数据源的数据，以便在数据分析和数据挖掘（或知识发现）的基础上，更好的来做决策 支持。数据库应用从广义上可分为事务处理和决策支持两类。事务处理系统现在正被广泛使 用

25、，并且公司已经积累了大量由这类系统产生的信息。数据挖掘这个概念广义上讲是指从大量数据中发现有关信息，或“发现知识” 。与人 工智能中的知识发现类似，数据挖掘试图自动从数据中发现统计规则和模式。但是，数据 挖掘和机器学习的不同在于它处理的是大量数据，它们主要存储在磁盘上。从数据库中发现的知识可以用一个规则集表示。我们用如下两个模型之一从数据库中 发现规则： 在第一个模型中，用户直接参与知识发现的过程 在第二个模型中，系统通过检测数据的模式和相互关系，自动从数据库中发现知 识。有关自动发现规则的研究很大程度上是受人工智能领域在知识学习方面研究的影响。 其主要的区别在于数据库中处理的数据量，以及是否

26、需要访问磁盘。已经有一些具体的数 据挖掘算法用于高效地处理放在磁盘上的大量数据。规则发现的方式依赖于数据挖掘应用的类型。我们用两类应用阐述规则发现：分类和关联。2、空间和地理数据库空间数据库存储有关空间位置的信息，并且对高效查询和基于空间位置的索引提供支 持。有两种空间数据库特别重要： 设计数据库或计算机辅助设计 （CAD ）数据库是用于存储设计信息的空间数据库， 这些信息主要是关于物体（如建筑、汽车或是飞机）是如何构造的。另一个计算 机辅助设计数据库的重要例子是整合电路和电子设备设计图。 地理数据库是用于存储地理信息（如地图）的空间数据库。地理数据库常称为地 理信息系统。地理数据本质上是空间

27、的，但与设计数据相比在几个方面有所不同。地图和卫星图像 是地理数据的典型例子。地图不仅可提供位置信息，如边界、河流和道路，而且还可以提 供许多和位置相关的详细信息，如海拔、土壤类型、土地使用和年降雨量。地理数据可以 分为两类： 光栅数据 （这种数据由二维或更高维的位图或像素图组成） 、矢量数据 （由基本 几何对象构成） 。地图数据常以矢量形式表示。3、多媒体数据库最近，有关多媒体数据（如图像、声音和视频）的数据库的研究很热门。现在多媒体 数据通常存储在数据库以外的文件系统中。当多媒体对象的数目相对较少时，数据库提供 的特点往往不那么重要。但是当存储的多媒体对象数目较多时，数据库的功能就变得重要 起来。 总之， 事务更新、 查询机制和索引也开始变的很重要。 多媒体对象常常有描述属性， 如指明它们是何时创建的、谁创建的，以及它们属于哪一类。构造这种多媒体对象的数据 库的方法之一是用数据存储描述属性，并且跟踪存储这些媒体对象