毕业设计(论文)外文翻译(原文)

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2024年5月2日发(作者：)

毕业设计（论文）——外文翻译（原文）

NEW APPLICATION OF DATABASE

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 processing

for banks and stock exchanges, sales and reservations for a variety of businesses, 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, businesses 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 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 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 process 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 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 and associations.

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 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 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. Issues 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 missing, 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

assumption of central control and administration is not entirely correct:

increasingly widespread use of personal computers, and, more important, of laptop or

“notebook” computers.

development of a relatively low-cost wireless digital communication infrastructure, base

on wireless local-area networks, cellular digital packet networks, and other technologies.

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

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 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 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-processing systems. Proposed extensions to SQL, such as

the cube operation, help to support generation of summary data. Data mining seeks to discover

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