Everyone knows that the Internet has changed how businesses operate, governments function, and people live. But a new, less visible technological trend is just as transformative: “big data”. Big data starts with the fact that there is a lot more information floating around these days than ever before, and it is being put to extraordinary new uses. Big data is distinct from the Internet, although the Web makes it much easier to collect and share data. Big data is about more than just communication: the idea is that we can learn from a large body of information things that we could not comprehend when we used only smaller amounts.
Big data will have implications far beyond medicine and consumer goods: it will profoundly change how governments work and change the nature of politics. When it comes to generating economic growth, providing public services, or fighting wars, those who can harness big data effectively will enjoy a significant advantage over others. So far, the most exciting work is happening at the municipal(城市的) level, where it is easier to access data and to experiment with the information. In an effort spearheaded by New York City mayor's office, the city is using big data to improve public services and lower costs. One example is a new fire-prevention strategy.
Illegally subdivided buildings are far more likely than other buildings to go up in flames. The city gets 25，000 complaints about overcrowded buildings a year, but it has only 200 inspectors to respond. A small team of analytics specialists in the mayor's office thought that big data could help resolve this imbalance between needs and resources. The team created a database of all buildings in the city and augmented it with data collected by 19 city agencies: records of tax, abnormal conditions in utility(公用设施) usage, service cuts, missed payments, ambulance visits, local crime rates, and more. Then, they compared this database to records of building fires from the past five years, ranked by severity, hoping to uncover correlations. Not surprisingly, among the predictors of a fire were the type of building and the year it was built. Less expected, however, was the finding that buildings obtaining permits for exterior brickwork(砖砌外墙) correlated with lower risks of severe fire.
Using all this data allowed the team to create a system that could help them
determine which overcrowding complaints needed urgent attention. None of the buildings' characteristics they recorded caused fires; rather, they correlated with an increased or decreased risk of fire. That knowledge has proved immensely valuable: in the past, building inspectors issued vacate(搬出) orders in 13 percent of their visits; using the new method, that figure rose to 70 percent—a huge efficiency gain.
Of course, insurance companies have long used similar methods to estimate fire risks, but they mainly rely on only a handful of factors and usually ones that intuitively(凭直觉地) correspond with fires. By contrast, New York City's big—data approach was able to examine many more variables, including ones that would not at first seem to have any relation to fire risk. And the city's model was cheaper and faster, since it made use of existing data. Most important, the big-data predictions are probably more on target, too.
Big data is also helping increase the transparency(透明度) of democratic governance. A movement has grown up around the idea of “open data”， which goes beyond the freedom-of-information laws that are now commonplace in developed democracies. Supporters call on governments to make the vast amounts of data that they hold easily available to the public. The United States has been at the forefront, with its Data.gov website, and many other countries have followed.
At the same time as governments promote the use of big data, they will also need to protect citizens against unhealthy market dominance(垄断). Companies such as Google, Amazon, and Facebook—as well as lesser-known “data brokers”， such as Acxiom and Experian—are amassing vast amounts of information on everyone and everything. Antitrust laws protect against the dominance of markets for goods and services such as software or media outlets, because the sizes of the markets for those goods are relatively easy to estimate. But how should governments apply antitrust rules to big data, a market that is hard to define and that is constantly changing form? Meanwhile, privacy will become an even bigger worry, since more data will almost certainly lead to more exposure of private information, a downside of big data that current technologies and laws seem unlikely to prevent.
( )62. According to the passage, what is the final purpose of New York City
mayor's office database?
A. To find which buildings have lower fire risks. B. To improve public services and lower costs. C. To help building inspectors respond to emergencies. D. To compare it to previous records of building fires.
( )63. Compared with those of insurance companies, New York City's big data fire predictions have all the following advantages EXCEPT that ________．
A. they correspond more intuitively with fires B. they examine more variables
C. they are cheaper and faster D. they are more accurate
( )64. Why is it difficult for governments to apply antitrust laws to big data?
A. Because they only prevent the dominance of markets for goods and services. B. Because using big data is more important than protecting private information. C. Because the freedom-of-information laws are not commonplace in all countries. D. Because the size of the market for big data is difficult to estimate. ( )65. The underlined word “downside” in the last paragraph is closest in meaning to ________．
A. complaint B. drawback C. consumption D. feature
D
Hartley got to Central Station nearly an hour before his train was due to leave. A lifetime in the theatre had given him a healthy—indeed excessive(过分的)—sense of punctuality; a lifetime of unwanted cups of coffee, constant checking of the time, yet another turn around the clock before that all too often pointless, tiresome audition(试镜)．
Hartley was 75—pretty fit for his age, legs holding up, memory still ticking over nicely—though the occasions for punctuality were now rather fewer. But he was a creature of habit and couldn't change now.
He repaired to the restaurant, purchased a coffee and a blueberry muffin, tried and failed to find a litter-free table. The coffee was awful, the muffin was stale
—but the coffee was always awful, the muffin always stale. Hartley refused to let himself be annoyed. His visit to the city had not been without its pleasures. Lunch with an old actor-chum(好友), then a film—regrettably not utilizing(利用) his own talents—had rounded out an agreeable day.
Hartley was a good actor, although the calls on his talents were now infrequent. But really, he thought draining his awful coffee, he'd had a reasonably good career. Something to be proud of. But he'd never had that break-through part.
He headed for his platform. Just as the train was about to pull out a man ran down the platform, jumped aboard as the door slammed shut and sank into the seat next to Hartley.
“Cutting it a bit fine”， he said.
“Indeed”， Hartley replied. “A close run thing”．
The man—forty-ish, amiable looking—gave him an amused glance.
This brief exchange served as an adequate ice-breaker and they chatted their way through the outer suburbs and into the countryside. Having satisfactorily disposed of the sad state of the railways, country versus city living, his neighbour asked Hartley what he did—or had done—for a living.
Hartley hated telling people he was an actor. He was not ashamed of his job. Not in the least, but he had long tired of reactions ranging from “what have I seen you in” to “how do you learn all those lines”．
So in situations like this he simply selected an occupation from a former role. Bit risky, of course. You say you're a doctor and find yourself meeting the quizzical(疑问的) gaze of a heart surgeon. But he'd never been caught out and it was a harmless enough game, Hartley felt. It amused him, and he'd given some damn good performances too.
“I'm a lawyer”， he replied. “Retired several years ago. Property law. Bit of criminal stuff”．
The train was slowing down. The man glanced out of the window. “My station. I_had_you_quite_wrong_then”．
He stood and took down his briefcase from the overhead rack.
“Yes, I'd have said you were an actor. The voice especially. Still, lawyers are actors in a way, don't you think? Plenty of drama in a courtroom.”
The train drew into the station.
“I'm a film director. Casting a feature at the moment. You study faces. On the train. Everywhere. Always on the lookout. Anyway, enjoyed our chat. Bye.”
( )66. The writer refers to Hartley's sense of punctuality as “excessive”. Which of the following phrases fails to describe his sense of time?
A. Nearly an hour before his train. B. A lifetime of unwanted cups of coffee.
C. Another turn around the clock before that audition. D. A creature of habit that couldn't change now.
( )67. What did Hartley think of his not telling his occupation? A. Harmful to his acting career. B. Amusing despite the risk. C. Helpful to protect his identity. D. Upsetting when caught out.
( )68. What can we learn about Hartley's travel companion from the underlined sentence “I had you quite wrong then”？
A. He assumed Hartley had given another answer. B. He understood Hartley's profession was acting. C. He thought Hartley practiced a different profession. D. He mistook Hartley for another person.
( )69. The use of the word “Bye” at the end of the story ________． A. shows the readers how unexpectedly Hartley's career ends B. describes Hartley's shock on finding the man is a director C. confirms Hartley's lack of luck in spite of his acting skills D. proves the man will reconsider giving Hartley a chance to act ( )70. Which of the following can be the best title of the story? A. Acting up B. Employing Talents
C. Selecting an Occupation D. Casting a Feature
第Ⅱ卷(非选择题 共35分)
第四部分：任务型阅读(共10小题；每小题1分，满分10分)