Big Data: A Revolution That Will Transform How We Live, Work and Think 本书作者Viktor Mayer-Schonberger签名版 限量独家销售 网盘下载 pdf 免费 2025 在线 epub 电子版

　　A revelatory exploration of the hottest trend in technology

and the dramatic impact it will have on the economy, science, and

society at large.

Which paint color is most

likely to tell you that a used car is in good shape? How can

officials identify the most dangerous New York City manholes before

they explode? And how did Google searches predict the spread of the

H1N1 flu outbreak?

The key to answering these

questions, and many more, is big data. “Big data” refers to our

burgeoning ability to crunch vast collections of information,

analyze it instantly, and draw sometimes profoundly surprising

conclusions from it. This emerging science can translate myriad

phenomena—from the price of airline tickets to the text of millions

of books—into searchable form, and uses our increasing computing

power to unearth epiphanies that we never could have seen before. A

revolution on par with the Internet or perhaps even the printing

press, big data will change the way we think about business,

health, politics, education, and innovation in the years to come.

It also poses fresh threats, from the inevitable end of privacy as

we know it to the prospect of being penalized for things we haven’t

even done yet, based on big data’s ability to predict our future

behavior.

In this brilliantly clear,

often surprising work, two leading experts explain what big data

is, how it will change our lives, and what we can do to protect

ourselves from its hazards. Big Data is the first big book about

the next big thing.

1  NOW

2  MORE

3  MESSY

4  CORRELATION

5  DATAFICATION

6  VALUE

7  IMPLICATIONS

8  RISKS

9  CONTROL

10  NEXT

Notes

Bibliography

Acknowledgments

Index

　　VIKTOR MAYER-SCH?NBERGER is Professor of Internet Governance

and Regulation at the Oxford Internet Institute, Oxford University.

A widely recognized authority on big data, he is the author of over

a hundred articles and eight books, of which the most recent is

Delete: The Virtue of Forgetting in the Digital Age. He is on the

advisory boards of corporations and organizations around the world,

including Microsoft and the World Economic Forum.

　　KENNETH CUKIER is the Data

Editor of the Economist and a prominent commentator on developments

in big data. His writings on business and economics have appeared

in Foreign Affairs, the New York Times, the Financial Times, and

elsewhere. 

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　　1

　　NOW

　　IN 2009 A NEW FLU virus was discovered. Combining elements of the

viruses that cause bird flu and swine flu, this new strain, dubbed

H1N1, spread quickly. Within weeks, public health agencies around

the world feared a terrible pandemic was under way. Some

commentators warned of an outbreak on the scale of the 1918 Spanish

flu that had infected half a billion people and killed tens of

millions. Worse, no vaccine against the new virus was readily

available. The only hope public health authorities had was to slow

its spread. But to do that, they needed to know where it already

was.

   In the United States, the Centers for Disease

Control and Prevention (CDC) requested that doctors inform them of

new flu cases. Yet the picture of the pandemic that emerged was

always a week or two out of date. People might feel sick for days

but wait before consulting a doctor. Relaying the information back

to the central organizations took time, and the CDC only tabulated

the numbers once a week. With a rapidly spreading disease, a

two-week lag is an eternity. This delay completely blinded public

health agencies at the most crucial moments.

   As it happened, a few weeks before the H1N1 virus

made headlines, engineers at the Internet giant Google published a

remarkable paper in the scientific journal Nature. It created a

splash among health officials and computer scientists but was

otherwise overlooked. The authors explained how Google could

“predict” the spread of the winter flu in the United States, not

just nationally, but down to specific regions and even states. The

company could achieve this by looking at what people were searching

for on the Internet. Since Google receives more than three billion

search queries every day and saves them all, it had plenty of data

to work with.

   Google took the 50 million most common search terms

that Americans type and compared the list with CDC data on the

spread of seasonal flu between 2003 and 2008. The idea was to

identify people infected by the flu virus by what they searched for

on the Internet. Others had tried to do this with Internet search

terms, but no one else had as much data, processing power, and

statistical know-how as Google.

   While the Googlers guessed that the searches might

be aimed at getting flu information?—?typing phrases like “medicine

for cough and fever”?—?that wasn’t the point: they didn’t know, and

they designed a system that didn’t care. All their system did was

look for correlations between the frequency of certain search

queries and the spread of the flu over time and space. In total,

they processed a staggering 450 million different mathematical

models in order to test the search terms, comparing its predictions

against actual flu cases from the CDC in 2007 and 2008. And they

struck gold: their software found a combination of 45 search terms

that, when used together in a mathematical model, had a strong

correlation between their prediction and the official figures

nationwide. Like the CDC, they could tell where the flu had spread,

but unlike the CDC they could tell it in near real-time, not a week

or two after the fact.

   Thus when the H1N1 crisis struck in 2009, Google’s

system proved to be a more useful and timely indicator than

government statistics with their natural reporting lags. Public

health officials were armed with valuable information.

   Strikingly, Google’s method does not involve

distributing mouth swabs or contacting physicians’ offices.

Instead, it is built on “big data”?—?the ability of society to

harness information in novel ways to produce useful insights or

goods and services of significant value. With it, by the time the

next pandemic comes around, the world will have a better tool at

its disposal to predict and thus prevent its spread.

Public health is only one area where big data is making a big

difference. Entire business sectors are being reshaped by big data

as well. Buying airplane tickets is a good example.

   In 2003 Oren Etzioni needed to fly from Seattle to

Los Angeles for his younger brother’s wedding. Months before the

big day, he went online and bought a plane ticket, believing that

the earlier you book, the less you pay. On the flight, curiosity

got the better of him and he asked the fellow in the next seat how

much his ticket had cost and when he had bought it. The man turned

out to have paid considerably less than Etzioni, even though he had

purchased the ticket much more recently. Infuriated, Etzioni asked

another passenger and then another. Most had paid less.

   For most of us, the sense of economic betrayal

would have dissipated by the time we closed our tray tables and put

our seats in the full, upright, and locked position. But Etzioni is

one of America’s foremost computer scientists. He sees the world as

a series of big-data problems?—?ones that he can solve. And he has

been mastering them since he graduated from Harvard in 1986 as its

first undergrad to major in computer science.

   From his perch at the University of Washington, he

started a slew of big-data companies before the term “big data”

became known. He helped build one of the Web’s first search

engines, MetaCrawler, which was launched in 1994 and snapped up by

InfoSpace, then a major online property. He co-founded Netbot, the

first major comparison-shopping website, which he sold to Excite.

His startup for extracting meaning from text documents, called

ClearForest, was later acquired by Reuters.

   Back on terra firma, Etzioni was determined to

figure out a way for people to know if a ticket price they see

online is a good deal or not. An airplane seat is a commodity: each

one is basically indistinguishable from others on the same flight.

Yet the prices vary wildly, being based on a myriad of factors that

are mostly known only by the airlines themselves.

   Etzioni concluded that he didn’t need to decrypt

the rhyme or reason for the price differences. Instead, he simply

had to predict whether the price being shown was likely to increase

or decrease in the future. That is possible, if not easy, to do.

All it requires is analyzing all the ticket sales for a given route

and examining the prices paid relative to the number of days before

the departure.

   If the average price of a ticket tended to

decrease, it would make sense to wait and buy the ticket later. If

the average price usually increased, the system would recommend

buying the ticket right away at the price shown. In other words,

what was needed was a souped-up version of the informal survey

Etzioni conducted at 30,000 feet. To be sure, it was yet another

massive computer science problem. But again, it was one he could

solve. So he set to work.

   Using a sample of 12,000 price observations that

was obtained by “scraping” information from a travel website over a

41-day period, Etzioni created a predictive model that handed its

simulated passengers a tidy savings. The model had no understanding

of why, only what. That is, it didn’t know any of the variables

that go into airline pricing decisions, such as number of seats

that remained unsold, seasonality, or whether some sort of magical

Saturday-night-stay might reduce the fare. It based its prediction

on what it did know: probabilities gleaned from the data about

other flights. “To buy or not to buy, that is the question,”

Etzioni mused. Fittingly, he named the research project Hamlet.

   The little project evolved into a venture

capital-backed startup called Farecast. By predicting whether the

price of an airline ticket was likely to go up or down, and by how

much, Farecast empowered consumers to choose when to click the

“buy” button. It armed them with information to which they had

never had access before. Upholding the virtue of transparency

against itself, Farecast even scored the degree of confidence it

had in own predictions and presented that information to users

too.

   To work, the system needed lots of data. To improve

its performance, Etzioni got his hands on one of the industry’s

flight reservation databases. With that information, the system

could make predictions based on every seat on every flight for most

routes in American commercial aviation over the course of a year.

Farecast was now crunching nearly 200 billion flight-price records

to make its predictions. In so doing, it was saving consumers a

bundle.

   With his sandy brown hair, toothy grin, and

cherubic good looks, Etzioni hardly seemed like the sort of person

who would deny the airline industry millions of dollars of

potential revenue. In fact, he set his sights on doing even more

than that. By 2008 he was planning to apply the method to other

goods like hotel rooms, concert tickets, and used cars: anything

with little product differentiation, a high degree of price

variation, and tons of data. But before he could hatch his plans,

Microsoft came knocking on his door, snapped up Farecast for around

$110 million, and integrated it into the Bing search engine. By

2012 the system was making the correct call 75 percent of the time

and saving travelers, on average, $50 per ticket.

   Farecast is the epitome of a big-data company and

an example of where the world is headed. Etzioni couldn’t have

built the company five or ten years earlier. “It would have been

impossible,” he says. The amount of computing power and storage he

needed was too expensive. But although changes in technology have

been a critical factor making it possible, something more important

changed too, something subtle. There was a shift in mindset about

how data could be used.

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　　本书英文版10%精华内容中文版未作翻译，引用大量案例如Amazon、Google、Twitter、Facebook、苹果及VISA等阐述了“

掌握大数据就掌握了一切”的观点，是关于大数据方面*好的书。

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　　"Every decade, there are a handful of books that change the way

you look at everything. This is one of those books. Society has

begun to reckon the change that big data will bring. This book is

an incredibly important start."

　　—Lawrence Lessig, Roy L. Furman Professor of Law, Harvard Law

School, and author of Remix and Free Culture

　　"This brilliant book cuts through the mystery and the hype

surrounding big data.

　　A must-read for anyone in business, information technology,

public policy, intelligence, and medicine. And anyone else who is

just plain curious about the future."

　　—John Seely Brown, former Chief Scientist, Xerox Corp., and head

of Xerox Palo Alto Research Center

　　"Big Data breaks new ground in identifying how today’s avalanche

of information fundamentally shifts our basic understanding of the

world. Argued boldly and written beautifully, the book clearly

shows how companies can unlock value, how policymakers need to be

on guard, and how everyone’s cognitive models need to

change."

　　—Joi Ito, Director of the MIT Media Lab

　　"Big Data is a must-read for anyone who wants to stay ahead of

one of the key trends defining the future of business."

　　—Marc Benioff, Chairman and CEO, salesforce.com

　　"An optimistic and practical look at the Big Data revolution —

just the thing to get your head around the big changes already

underway and the bigger changes to come."

　　—Cory Doctorow, boingboing.com

　　"Just as water is wet in a way that individual water molecules

aren’t, big data can reveal information in a way that individual

bits of data can’t. The authors show us the surprising ways that

enormous, complex, and messy collections of data can be used to

predict everything from shopping patterns to flu outbreaks."

　　—Clay Shirky, author of Cognitive Surplus and Here Comes

Everybody

　　"The book teems with great insights on the new ways of harnessing

information, and offers a convincing vision of the future. It is

essential reading for anyone who uses — or is affected by — big

data."

　　—Jeff Jonas, IBM Fellow & Chief Scientist, IBM Entity

Analytics

媒体评论

"Every decade, there are a handful of books that change the way

you look at everything. This is one of those books. Society has

begun to reckon the change that big data will bring. This book is

an incredibly important start."

—Lawrence Lessig, Roy L. Furman Professor of Law, Harvard Law

School, and author of Remix and Free Culture

"This brilliant book cuts

through the mystery and the hype surrounding big data.

A must-read for anyone in business, information technology,

public policy, intelligence, and medicine. And anyone else who is

just plain curious about the future."

—John Seely Brown, former Chief Scientist, Xerox Corp., and head

of Xerox Palo Alto Research Center

"Big Data breaks new ground in

identifying how today’s avalanche of information fundamentally

shifts our basic understanding of the world. Argued boldly and

written beautifully, the book clearly shows how companies can

unlock value, how policymakers need to be on guard, and how

everyone’s cognitive models need to change."

—Joi Ito, Director of the MIT Media Lab

"Big Data is a must-read for

anyone who wants to stay ahead of one of the key trends defining

the future of business."

—Marc Benioff, Chairman and CEO, salesforce.com

"An optimistic and practical

look at the Big Data revolution — just the thing to get your head

around the big changes already underway and the bigger changes to

come."

—Cory Doctorow, boingboing.com

"Just as water is wet in a way

that individual water molecules aren’t, big data can reveal

information in a way that individual bits of data can’t. The

authors show us the surprising ways that enormous, complex, and

messy collections of data can be used to predict everything from

shopping patterns to flu outbreaks."

—Clay Shirky, author of Cognitive Surplus and Here Comes

Everybody

"The book teems with great

insights on the new ways of harnessing information, and offers a

convincing vision of the future. It is essential reading for anyone

who uses — or is affected by — big data."

—Jeff Jonas, IBM Fellow & Chief Scientist, IBM Entity

Analytics

书籍介绍

Since Aristotle, we have fought to understand the causes behind everything. But this ideology is fading. In the age of big data, we can crunch an incomprehensible amount of information, providing us with invaluable insights about the what rather than the why. We're just starting to reap the benefits: tracking vital signs to foresee deadly infections, predicting building fires, anticipating the best moment to buy a plane ticket, seeing inflation in real time and monitoring social media in order to identify trends. But there is a dark side to big data. Will it be machines, rather than people, that make the decisions? How do you regulate an algorithm? What will happen to privacy? Will individuals be punished for acts they have yet to commit? In this groundbreaking and fascinating book, two of the world's most-respected data experts reveal the reality of a big data world and outline clear and actionable steps that will equip the reader with the tools needed for this next phase of human evolution.