谁能帮我翻译这篇文章A relational database management system should be

谁能帮我翻译这篇文章
A relational database management system should be able to query data, update data and control data. In order to apply this function, there must be a language to describe what the client wants, that is SQL language. SQL is both a Data Definition Language (DDL) and a Data Manipulation Language (DML). As a DDL, it allows a database administrator or database designer to define tables, create views, etc. As a DML, it allows an end user to retrieve information from tables. It came from an IBM Research project entitled "SEQUEL" where the intent was to create a structured English-like query language to interface to the early System R database system. Along with QUEL, SQL was the first high level declarative database language. There are two ways to use SQL, which are directly using SQL online and by using high level language such as C+, FORTRAN, etc.
Database application that may commonly be used are Oracle, Informix, Sybase, Microsoft Access, Postgresql, Mysql.
Finally, let us see what data mining is. Organization are accumulating vast volumes of data because of the implementation of technology that makes it easier and cheaper to collect data. The world’s data are estimated to be doubling every 20 months, and many large companies now routinely manage terabytes(1012) of data. Thus, we can see that the growth of data is too fast compared to the ability of human to be able to analyze the information inside those massive data collection. We are drowning in data, but starving for knowledge! That’s why we need data mining.
Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.
Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery. Data mining is ready for application in the business community because it is supported by three technologies that are now sufficiently mature:

一个有关系的数据库管理系统应该能够询问数据,更新数据并且控制数据.为了运用这功能来描述,一定有一种语言什么顾客需要,是SQL语言.SQL是一种数据定义语言( DDL )和一种数据操纵语言( DML ).当时一DDL,它允许一个数据库管理人员或者数据库设计者定义桌子,作为一DML建立观点等等,它把一个终端用户允许到来自若干桌子的找回信息.它来自意图是建立一种构造的英语的查询语言到接口到早期系统R数据库系统的一IBM研究项目授权的“SEQUEL”.与QUEL一同,SQL是第一个高的水平宣言的数据库语言.是双向使用SQL,这直接使用联机的SQL和通过使用诸如C+的高级语言,FORTRAN等等
可能一般被使用的数据库应用是神谕,Informix,Sybase,微软接近,Postgresql,Mysql.
最后,让我们看到什么数据采矿.由于执行收集数据做出它更容易和便宜的技术组织积累巨大的卷的数据.world’s数据被估计每20月是加倍,而许多大的公司现在通常管理terabytes(1012 )数据.这样,我们能看到数据的增长与人类的能力相比信息在那些大量的数据收集内部分析.我们在数据中是溺水,但是渴望知识!为什么我们需要数据采矿的That’s.
数据采矿,抽取来自大的数据库的隐藏的预言性的信息,是巨大的潜力的一种强大的新技术到帮助公司着重于他们的数据仓库中的最重要的信息.数据采矿工具预言未来的趋势和行为,允许商业制定proactive,知识驾驶的决定.在分析被决定支援系统典型的回顾的工具提供的过去的事件以外被数据采矿行动提供的自动,预期的分析.数据采矿工具能回答商业问题在传统上,是时间消耗到决心.他们为了隐藏的模式擦亮数据库,找到专家可能错过,因为它在他们的期望之外躺的预言性的信息.
数据采矿技术产生于研究和产品开发的一个长的过程.当商业数据首先被存储在计算机上时,这进化开始,在数据接近中继续改进,而更最近,产生在实时允许用户通过他们的数据驾驶的技术.数据采矿在回顾的数据接近和导航以外把这进化的过程拿到预期和proactive信息发送.数据采矿为对在商界中的运用作好准备因为它得到是现在充分地成熟的的三种技术的支持：
要给分啊,我很辛苦的