毕业设计（论文）——外文翻译（原文）
NEWAPPLICATIONOFDATABASE
Relational databases have been in use for over two decades.A large portion of the applications of relational databases have been in the commercial world, supporting such tasks as transaction proceing for banks and stock exchanges, sales and reservations for a variety of businees, and inventory and payroll for almost of all companies.We study several new applications, which have become increasingly important in recent years.
First.Decision-support system
As the online availability of data has grown, businees have begun to exploit the available data to make better decisions about increase sales.We can extract much information for decision support by using simple SQL queries.Recently however, people have felt the need for better decision support based on data analysis and data mining, or knowledge discovery, using data from a variety of sources.
Database applications can be broadly claified into transaction proceing and decision support.Transaction-proceing systems are widely used today, and companies have accumulated a vast amount of information 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 patterns 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 first model, the user is involved directly in the proce of knowledge discovery.
In the second model, the system is responsible for automatically discovering knowledge from the database, by detecting patterns and correlations in the data.
Work on automatic discovery of rules has been influenced strongly by work in the artificial-intelligence community on machine learning.The main differences lie in the volume of data handled in databases, and in the need to acce disk.Specialized data-mining algorithms have been developed to handle large volumes of disk-resident data efficiently.
The manner in which rules are discovered depends on the cla of data-mining application.We illustrate rule discovery using two application claes: claification and aociations.
Second.Spatial and Geographic Databases
Spatial 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 particularly important:
Design databases, or computer-aided-design (CAD) databases, are spatial databases used to store design information about how objects---such as buildings, cars or aircraft---are constructed.Other important examples of computer-aided-design databases are integrated-circuit and electronic-device layouts.
Geographic databases are spatial databases used to store geographic information, such as maps.Geographic 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 also much more detailed information aociated 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 constructed from basic geographic objects).Map data are often represented in vector format.
Third.Multimedia Databases
Recently, there has been 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.When 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.Iues such as transactional updates, querying facilities, and indexing then become important.Multimedia objects often have descriptive attributes, such as those indicating when 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 stored.
However, storing multimedia outside the database makes it harder to provide database functionality, such as indexing on 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 miing, or vice versa.It is therefore desirable to store the data themselves in the database.
Forth.Mobility and Personal Databases
Large-scale commercial databases have traditionally been stored in central computing facilities.In the case of distributed database applications, there has usually been strong central database and network administration.Two technology trends have combined to create applications in which this aumption of central control and administration 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 wirele digital communication infrastructure, base on wirele local-area networks, cellular digital packet networks, and other technologies.
Wirele computing creates a situation where machines no longer have fixed locations and network addrees.This complicates query proceing, since it becomes difficult to determine the optimal location 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 hotels, roadside services, and the like to motorists.Queries about services that are ahead on the current route must be proceed 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 scheduled 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.
Summary
Decision-support systems are gaining importance, as companies realize the value of the on-line data collected by their on-line transaction-proceing systems.Proposed extensions to SQL, such as the cube operation, help to support generation of summary data.Data mining seeks to discover
knowledge automatically, in the form of statistical rules and patterns from large databases.Data visualization systems help humans to discover such knowledge visually.
Spatial databases are finding increasing use today to store computer-aided design 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.Iues such as similarity-based retrieval and delivery of data at guaranteed rates are topics of current research.
Mobile computing systems have become common, leading to interest in database systems that can run on such systems.Query proceing 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，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的文件。
但是，将多媒体数据存储在数据库之外，使得难于提供数据库的功能，譬如基于实际多媒体数据内容的索引。此外这种情况还会造成不一致，譬如一个文件在数据库中做了记录，但其内容却丢失了；或其相反情况。因此我们更希望将数据本身存储在数据库中。
4、移动性和个人数据库
大型商用数据库传统上是存储在中央计算设备上的。在分布式数据库应用中，通常有强大的中央数据库和网络管理。然而以下这两个技术趋势的结合产生了一些应用，这些应用使中央控制和管理不再完全正确：
● 个人计算机越来越广泛的使用，其中更重要的 是便携式或“笔记本”计算机的使用。 ● 基于无限局域网、蜂窝数字包网络，以及其他技术成本相对低廉的无线数字通信基础设
施的发展。
无线计算使得计算机不必有固定的位置和网络地址这使得查询处理更加复杂，因为它难于决定实体化查询结果的最佳位置。某些情况下，用户的位置是一个查询参数。例如，一个旅客信息系统提供关于酒店、路边服务的信息及类似信息给乘车的旅客。有关当前道路前放服务的查询必须根据用户的位置、移动的方向及速度进行处理。
能源（电池能源）对应动计算机来说是有限的资源，这一限制影响了系统设计的许多方面。能源效率需求最有趣的结果之一的使用计划的数据广播来减少传输查询中移动系统的需求。越来越多的数据会放在由用户管理、而不是由数据库管理员管理的计算机上，并且这些计算机有时可能与网络断开连接。
5、总结
随着企业认识到联机事务处理系统收集的联机数据的价值，决策支持系统也越发变得重要了。现已提出SQL扩展，如cube操作，能帮助系统生成汇总数据。数据挖掘致力于从大数据库中自动发现统计规律和模式等知识。数据可视化系统帮助人们从视觉上发现这些知识。
目前，空间数据库正越来越多的被应用于存储计算机辅助设计数据和地理数据。设计数据基本上是以矢量数据的形式存储，而地理数据则包含矢量数据和光栅数据。
多媒体数据库正变得越来越重要。基于相似性的查询以及按可以确保的速率传送数据是当前研究的重要课题。
移动计算系统的普及使人们对这类系统上运行的数据库产生了浓厚的兴趣。在这类系统上的查询处理可能会设计在服务器端数据库上的查找。
