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


我們使用關(guān)系數(shù)據(jù)庫已經(jīng)有20多年了，關(guān)系數(shù)據(jù)庫應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處理、各種業(yè)務的銷售和預約，以及幾乎所有公司都需要的財產(chǎn)目錄和工資單管理。下面我們要研究幾個新的應用，近年來它們變得越來越重要。
1、決策支持系統(tǒng)
由于越來越多的數(shù)據(jù)可聯(lián)機獲得，企業(yè)已開始利用這些可獲得的數(shù)據(jù)來對自己的行動做出更好的決策，比如進什么貨，以及如何最好的吸引顧客以提高銷售額。我們可以通過使用簡單的SQL查詢語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（或知識發(fā)現(xiàn)）的基礎(chǔ)上，更好的來做決策支持。
數(shù)據(jù)庫應用從廣義上可分為事務處理和決策支持兩類。事務處理系統(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”。與人工智能中的知識發(fā)現(xiàn)類似，數(shù)據(jù)挖掘試圖自動從數(shù)據(jù)中發(fā)現(xiàn)統(tǒng)計規(guī)則和模式。但是，數(shù)