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 of

random-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 phenomenon 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 factory

workers 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 Analytics: The

New Science of Winning” (Harvard Business School Press, 2007).

The entry 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。在20世纪60年代和70年代，“运筹学”结合计算能力和数学主要是让工厂生产的工作更有效率。而且在那一时期，“决策支持”软件的目的是帮助管理人员更加智能的使用在大计算文件-数据库-信息正成为企业普遍。

但是早期的努力主要限于信息访问和报告制度，托马斯H达文波特说，Babson学院的教授。的数量和质量的数据通常是不够的，他指出，这个软件不能做先进的优化以及今天的预测运算。

更快，更廉价的计算和数字格式的信息来源充足-从企业资源规划系统，点销售终端设备和网站弹拨-意味着大多数公司现在可以用工具做一种有竞争力的分析，只有相对少数精英公司可以做过去。 “这是真正开始成为主流，”达文波特先生说，公司与珍妮湾哈里斯“的作者在分析竞争：赢得新科学”（哈佛商学院出版社，2007）。门槛，他说，“不再是技术，而是你有主管谁明白这一点。”

There are plenty who do. Big retailers like Wal-Mart Stores and

Kohl’s use today’s advanced computing and math to more

accurately predict what sizes of clothes should go to what stores.

Harrah’s and other casinos decipher slot-machine results to

optimize customer traffic and profits, and they use face-recognition

software to identify people with criminal records. And Stockholm

and other cities use traffic data and patterns to determine

“congestion pricing.”

In the financial industry, Capital One and other banks mine all

kinds of transaction data to identify, and stop, fraudulent

transactions. And Cemex, the big cement company, uses global

positioning satellite locators and traffic and weather data to

improve delivery-time performance in Mexico.

In the last year or so, Whirlpool, the appliance maker, has begun

using new analytics software to automatically scan warranty

reports as well as manufacturing, supplier, sales and service data to

try to further trim its warranty costs and improve quality. That is

no small task, since it sells an average of 25,000 washing machines

a day, for example. “A human being cannot see and detect all those

trends,” says John Kerr, the general manager for global quality.

有很多谁做的。大型零售商如沃尔玛和科尔的使用当今的先进计算和数学来更准确的预测多大尺寸的衣服应该送往哪些商店。哈拉斯和其他的赌场吃角子老虎机导致优化客户的流量和利润，并且他们使用脸部识别软件鉴别有犯罪纪录的人。和斯德哥尔摩等城市使用交通流量数据和模式来确定“道路交通拥堵收费。”

在金融行业，Capital One和其他的银行挖掘各种交易数据，以确定，停止，欺诈交易。和Cemex公司，大水泥企业，利用全球卫星定位系统，交通和天气数据来提高交付时间在墨西哥的表现。

在过去一年多，惠而浦，设备制造商已经开始采用新的分析软件来自动扫描保证报告，以及生产，供应，销售和服务数据来尝试进一步修整保证成本和提高质量。这是不小的任务，因为它销售2.5万台洗衣机，平均每天的例子。 “一个人不能看到和检测所有这些趋势，”约翰科尔，为全球质量总经理。

With the new computing tools, Whirlpool has trimmed by 30 to 90

days the time required to detect and fix parts or manufacturing

problems that cause defects. “The math is astounding,” Mr. Kerr

says.

The results help explain why business-intelligence software is one

of the hot markets in technology, supplied by companies like SAS,

Business Objects, Cognos, MicroStrategy and Information

Builders. In March, Oracle offered a hefty $3.3 billion for

Hyperion, a maker of business intelligence software. Microsoft

has entered the field as well.

But packaged software is not the only way to combine powerful

computing with deep math tools. The major technology services

companies, like I.B.M., Accenture and Hewlett-Packard, have

researchers, programmers and industry specialists doing this kind

of work for clients.

随着新的计算工具，Whirlpool减少了30到90天才能检测并修复部分或生产问题的时间造成的缺陷。 “数学真是惊人，”科尔先生说。

这一结果有助于解释为什么商业智能软件是在最火热的市场，由SAS公司提供的企业，Business Objects，Cognos，MicroStrategy和Information Builders的。今年3月，甲骨文公司提供了巨额美元，Hyperion公司，一家商业智能软件制造商33亿美元。微软已经进入了这个领域。

但是捆绑不是唯一的方法结合起来深入的数学工具，强大的计算。主要的技术服务公司，像IBM，Accenture和惠普，有研究人员，程序员和行业专家可以为客户这方面的工作。

Internet marketing and advertising is a social market made for the

use of heavy-duty computing and sophisticated mathematics.

Investment and start-up money is pouring into the market, and so

are many high-powered computing brains.

Basem Nayfeh has a Ph.D. from Stanford, where he did his

graduate research down the hall from one of Google’s founders,

Sergey Brin. Mr. Nayfeh’s thesis was on multiprocessor chips, and

he has worked in corporate labs in Silicon Valley on things as

diverse as climate and computer design.

Today Mr. Nayfeh, 37, is the chief technology officer of Revenue

Science, which tracks, analyzes and predicts online behavior to

help advertisers find people most likely to buy their products.

Many of his fellow computer wizards are in online marketing.

“If you asked any of us 5 or 10 years ago if we would be in

advertising,” he says, “none of us would have said yes.”

网络营销和广告是一种社会市场所作的使用重型和复杂的数学运算。投资和启动资金流入市场，因此有很多高性能的计算设备的大脑。

巴塞姆Nayfeh认为具有博士学位来自斯坦福大学，在那里他做了他自己下来，从谷歌的创始人之一谢尔盖布林大厅研究生研究。先生Nayfeh的论文是关于多处理器芯片，以及他在硅谷的一家公司实验室中的工作在不同的气候和计算机设计。

今天先生Nayfeh认为，37岁，是科学的收入，追踪，分析首席技术官和预测在线的行为帮助广告人寻找最有可能购买他们的产品。他的计算机奇才同事都是做在线营销。

“如果你问我们任何5年或10年前，如果我们将在广告，”他说，“我们没有一个人会说是的。”