毕业设计 数据库的新应用

1、毕业设计(论文)外文翻译(原文)摘要：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.关键词：论文,翻译类别：论文报告来源：牛档搜索（niudown.com）本文系牛档搜索（niudown.com）根据用户的指令自动搜索的结果，文中内涉及到的资料均来自互联网，用于学习交流经验，作品其著作权归原作者所有。不代表牛档搜索（niudown.com）赞成本文的内容或立场，牛档搜索（niudown.com）不对其付相应的法律责任！毕业设计（论文）外文翻译（原文）new application

2、of databaserelational 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 processing for banks and stock exchanges, sales and reservations for a variety of businesses, and

3、 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, businesses have begun to exploit the available data to make better decisions ab

4、out 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

5、 be broadly classified into transaction processing and decision support. transaction-processing 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 k

6、nowledge,” 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 di

7、sk.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 process of knowledge discovery.in the second model, the system is responsible for automatically discoveri

8、ng 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 n

9、eed to access 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 class of data-mining application. we illustrate rule discovery using two application classes: classification an

10、d 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 particularly important:design databases, or computer-aided-d

11、esign (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 datab

12、ases 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 on

13、ly location information -such as boundaries, rivers and roads-but 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, i

14、n two or more dimensions.), vector data (vector data are constructed from basic geographic objects). map data are often represented in vector format.third. multimedia databasesrecently, there has been much interest in databases that store multimedia data, such as images, audio, and video. today mult

15、imedia 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. issues such as

16、 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

17、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

18、. 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 database.forth. mobility and personal databaseslarge-scale commercial databases have traditionally been store

19、d 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 assumption of central control and administration is not entirely co

20、rrect: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 networks, and other technologi

21、es.wireless computing creates a situation where machines no longer have fixed locations and network addresses. this complicates query processing, 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 p

22、arameter of the query. a example is a travelers 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 processed based on knowledge of the users location, direction of motion, and speed.energy

23、(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. increasingl

24、y 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 systems are gaining importance, as companies realize the value of the on-line data collected by

25、their on-line transaction-processing 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

26、 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

27、growing in importance. issues 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 processing in such systems may involve loo

28、kups 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

29、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

30、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

31、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