畢業(yè)設(shè)計(jì)(論文)外文翻譯(原文).doc

畢業(yè)設(shè)計(jì)（論文）外文翻譯（原文）NEW APPLICATION 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 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 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-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 DatabasesRecently, 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 DatabasesLarge-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: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 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 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 (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.SummaryDecision-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 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. 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 lookups on server database.畢業(yè)設(shè)計(jì)（論文）外文翻譯（譯文）數(shù)據(jù)庫(kù)的新應(yīng)用我們使用關(guān)系數(shù)據(jù)庫(kù)已經(jīng)有20多年了，關(guān)系數(shù)據(jù)庫(kù)應(yīng)用中有很大一部分都用于商業(yè)領(lǐng)域支持諸如銀行和證券交易所的事務(wù)處理、各種業(yè)務(wù)的銷(xiāo)售和預(yù)約，以及幾乎所有公司都需要的財(cái)產(chǎn)目錄和工資單管理。下面我們要研究幾個(gè)新的應(yīng)用，近年來(lái)它們變得越來(lái)越重要。1、決策支持系統(tǒng)由于越來(lái)越多的數(shù)據(jù)可聯(lián)機(jī)獲得，企業(yè)已開(kāi)始利用這些可獲得的數(shù)據(jù)來(lái)對(duì)自己的行動(dòng)做出更好的決策，比如進(jìn)什么貨，以及如何最好的吸引顧客以提高銷(xiāo)售額。我們可以通過(guò)使用簡(jiǎn)單的SQL查詢語(yǔ)句提供大量用于決策支持的信息。但是，人們最近感到需要使用多種數(shù)據(jù)源的數(shù)據(jù)，以便在數(shù)據(jù)分析和數(shù)據(jù)挖掘（或知識(shí)發(fā)現(xiàn)）的基礎(chǔ)上，更好的來(lái)做決策支持。數(shù)據(jù)庫(kù)應(yīng)用從廣義上可分為事務(wù)處理和決策支持兩類。事務(wù)處理系統(tǒng)現(xiàn)在正被廣泛使用，并且公司已經(jīng)積累了大量由這類系統(tǒng)產(chǎn)生的信息。數(shù)據(jù)挖掘這個(gè)概念廣義上講是指從大量數(shù)據(jù)中發(fā)現(xiàn)有關(guān)信息，或“發(fā)現(xiàn)知識(shí)”。與人工智能中的知識(shí)發(fā)現(xiàn)類似，數(shù)據(jù)挖掘試圖自動(dòng)從數(shù)據(jù)中發(fā)現(xiàn)統(tǒng)計(jì)規(guī)則和模式。但是，數(shù)據(jù)挖掘和機(jī)器學(xué)習(xí)的不同在于它處理的是大量數(shù)據(jù)，它們主要存儲(chǔ)在磁盤(pán)上。從數(shù)據(jù)庫(kù)中發(fā)現(xiàn)的知識(shí)可以用一個(gè)規(guī)則集表示。我們用如下兩個(gè)模型之一從數(shù)據(jù)庫(kù)中發(fā)現(xiàn)規(guī)則： 在第一個(gè)模型中，用戶直接參與知識(shí)發(fā)現(xiàn)的過(guò)程 在第二個(gè)模型中，系統(tǒng)通過(guò)檢測(cè)數(shù)據(jù)的模式和相互關(guān)系，自動(dòng)從數(shù)據(jù)庫(kù)中發(fā)現(xiàn)知識(shí)。有關(guān)自動(dòng)發(fā)現(xiàn)規(guī)則的研究很大程度上是受人工智能領(lǐng)域在知識(shí)學(xué)習(xí)方面研究的影響。其主要的區(qū)別在于數(shù)據(jù)庫(kù)中處理的數(shù)據(jù)量，以及是否需要訪問(wèn)磁盤(pán)。已經(jīng)有一些具體的數(shù)據(jù)挖掘算法用于高效地處理放在磁盤(pán)上的大量數(shù)據(jù)。規(guī)則發(fā)現(xiàn)的方式依賴于數(shù)據(jù)挖掘應(yīng)用的類型。我們用兩類應(yīng)用闡述規(guī)則發(fā)現(xiàn)：分類和關(guān)聯(lián)。2、空間和地理數(shù)據(jù)庫(kù)空間數(shù)據(jù)庫(kù)存儲(chǔ)有關(guān)空間位置的信息，并且對(duì)高效查詢和基于空間位置的索引提供支持。有兩種空間數(shù)據(jù)庫(kù)特別重要： 設(shè)計(jì)數(shù)據(jù)庫(kù)或計(jì)算機(jī)輔助設(shè)計(jì)（CAD）數(shù)據(jù)庫(kù)是用于存儲(chǔ)設(shè)計(jì)信息的空間數(shù)據(jù)庫(kù)，這些信息主要是關(guān)于物體（如建筑、汽車(chē)或是飛機(jī)）是如何構(gòu)造的。另一個(gè)計(jì)算機(jī)輔助設(shè)計(jì)數(shù)據(jù)庫(kù)的重要例子是整合電路和電子設(shè)備設(shè)計(jì)圖。 地理數(shù)據(jù)庫(kù)是用于存儲(chǔ)地理信息（如地圖）的空間數(shù)據(jù)庫(kù)。地理數(shù)據(jù)庫(kù)常稱為地理信息系統(tǒng)。地理數(shù)據(jù)本質(zhì)上是空間的，但與設(shè)計(jì)數(shù)據(jù)相比在幾個(gè)方面有所不同。地圖和衛(wèi)星圖像是地理數(shù)據(jù)的典型例子。地圖不僅可提供位置信息，如邊界、河流和道路，而且還可以提供許多和位置相關(guān)的詳細(xì)信息，如海拔、土壤類型、土地使用和年降雨量。地理數(shù)據(jù)可以分為兩類：光柵數(shù)據(jù)（這種數(shù)據(jù)由二維或更高維的位圖或像素圖組成）、矢量數(shù)據(jù)（由基本幾何對(duì)象構(gòu)成）。地圖數(shù)據(jù)常以矢量形式表示。3、多媒體數(shù)據(jù)庫(kù)最近，有關(guān)多媒體數(shù)據(jù)（如圖像、聲音和視頻）的數(shù)據(jù)庫(kù)的研究很熱門(mén)?，F(xiàn)在多媒體數(shù)據(jù)通常存儲(chǔ)在數(shù)據(jù)庫(kù)以外的文件系統(tǒng)中。當(dāng)多媒體對(duì)象的數(shù)目相對(duì)較少時(shí)，數(shù)據(jù)庫(kù)提供的特點(diǎn)往往不那么重要。但是當(dāng)存儲(chǔ)的多媒體對(duì)象數(shù)目較多時(shí)，數(shù)據(jù)庫(kù)的