英语第八单元-中文翻译

data-mining moves into the mainstream 第八单元的中文基本翻译。

谨以献给我们这些英语不太好的同志分享。

数据挖掘移动成为主流RODNEY MONROE, the police chief in Richmond, Va., describes himself as a lifelong cop whose expertise is in fighting street crime, not in software. His own Web browsing, he says, mostly involves checking golf scores.罗德尼梦露，在里士满，弗吉尼亚州警察局长，自己描述为终身制的警察，专门打击街头犯罪，而不是软件上。

他自己的Web浏览，他说，主要是查看高尔夫成绩。

But shortly after he became chief in 2005, a crime analyst who had retired from the force convinced him to try some clever software. The programs cull through information that the department already collects, like “911” and police reports, but add new streams of data — about neighborhood demographics and payday schedules, for example, or about weather, traffic patterns and sports events —to try to predict where crimes might occur.不过，不久后他担任2005年，犯罪分析师谁从空军退役让他试一试一些聪明的软件。

通过信息，该署已收集像“911”和警察的报告程序扑杀，但增加了新的数据，街区人口统计和发工资的日期，例如，或者关于天气- ，交通模式和体育赛事-来预测犯罪会在哪里发生。

“It sounded nutty at first,” Mr. Monroe recalled, “but the more and more you get into it, the more sense it makes.”“起初这坚果，”门罗先生回忆说，“但是，越来越多的你就可以感受到，更有道理。

”The technology, for example, pointed to a high rate of robberies on paydays in Hispanic neighborhoods, where fewer people use banks and where customers leaving check-cashing stores were easy targets for robbers. Elsewhere, there were clusters ofrandom-gunfire incidents at certain times of night. So extra police were deployed in those areas when crimes were predicted.这项技术，例如，指出了高比率的劫案在西班牙人的街区，在那里很少人使用银行和发薪日，客户随身携带现金商店很容易成为抢劫的目标。

其他地方一样，都是随意开枪的事件集群在夜间的某些时候。

因此，额外的警力部署在那些罪行时预测的地区。

The crime rate in Richmond declined about 20 percent last year, and it is down again this year.The Richmond experience is part of a wave of sophisticated computing and mathematical analytics that is moving into the mainstream. Fueling the trend are the digitization of information, ever faster and cheaper computing, and the explosion of online networks and data collection.在Richmond市的犯罪率下降了百分之二十左右，去年，这是今年再次下降。

Richmond的经验是一个复杂的计算，并正在成为主流数学分析浪潮中的一部分。

加油的趋势是信息数字化，更快，更廉价的计算和在线网络和数据收集的爆炸。

The results, says Jon M. Kleinberg, a computer scientist at Cornell University, are a “revolution in measurement” and the “introduction of computing and algorithmic processes into the social sciences in a big way.” The phen omenon is strikingly evident in economics, business and crime prevention.结果，乔恩米克莱因伯格说，康奈尔大学的计算机科学家，是衡量一个“革命”，以及计算和算法处理“到大张旗鼓地引入到社会科学。

”这种现象是非常显着的经济，商业和预防犯罪。

Productivity research has traditionally focused on manufacturing, because the output of widgets and the headcount of factoryworkers were easy to measure, notes Erik Brynjolfsson, a professor at the Sloan School of Management at the Massachusetts Institute of Technology.The productivity of information workers —much of the nation’s work force — was shunted into a category that economists labeled “difficult to measure” and given short shrift.生产力研究传统上集中于制造业，因为部件的输出和工厂工人人数是很容易测量，记录埃里克布林约尔松，在斯隆管理学院，麻省理工学院教授。

对信息工作者的生产力-大部分国家的劳动力-被调整到被经济学家标注为“难以测量”，并表示稍微类别身上。

But the digital age, he says, has opened the door to detailed measurement of the labor of professionals and office workers who handle ideas and information from customers, suppliers, colleagues and marketers.“My thinking on productivity has completely changed,” says Mr. Brynjolfsson, who is also a research associate at the National Bureau of Economic Research.By tracking e-mail traffic, instant messages and other digital communications — stripped of personally identifiable information — he and other researchers are beginning to study the flow of work and ideas through the social networks inside companies — minute by minute, bit by bit.但是在数字时代，他说，开启了大门，这些专业人士和上班族谁处理来自客户，供应商，同事和营销思想和信息的劳动进行详细测量。

“我对生产力的认识已经完全改变了，”布林约尔松先生说，谁也是在美国国家经济研究局的研究人员。

通过跟踪电子邮件流量，即时信息和其他数字通讯-个人识别信息中剥离出来-他和其他研究人员开始研究通过企业内部的社会网络的工作和思想交流-一分钟一分钟，一点一滴。

“We’re really on the cusp of being able to understand what goes on inside corporations in a much more scientific way than ever before,” he said. “It’s similar to the way that the microscope opened up biology in the 17th century, so that you could see blood cells. Now, we can start to see bits of information as they flow through the organism of the corporation.”“我们就真的已经能够更好地理解事物在一个更加科学的方法对企业内部比以往任何时候，”他说。

“这是类似显微镜的方式开创了17世纪生物学，这样你可以看到血液细胞。

现在，我们可以开始看到比特的信息，他们通过公司的器官流。

“The desire to exploit computing and mathematical analytics is by no means new. In the 1960s and ’70s, “operations research” combined computing and math mainly to make factory production work more efficient. And in that period, “decision support” software was intended to help managers more intelligently use information in the big computing file cabinets — databases — that were becoming common in corporations.But the earlier efforts were limited mainly to information access and reporting systems, says Thomas H. Davenport, a professor at Babson College. The quantity and quality of data were typically inadequate, he notes, and the software could not do the advanced optimization and predictive calculations of today’s programs. Faster and cheaper computing and ample sources of information in digital form — plucked from enterprise resource planning systems, point-of-sale devices and Web sites — mean that most companies now have the tools to do the kind of competitive analytics that only a relative handful of elite companies could do in the past. “It’s really starting to become mainstream,” says Mr. Davenport,co-author with Jeanne G. Harris of “Competing on A nalytics: TheNew Science of Winning” (Harvard Business School Press, 2007). The e ntry barrier, he says, “is no longer technology, but whether you have executives who understand this.”的愿望，利用计算和数学分析能力的新的。