Chapter 3 MESSY - PowerPoint Presentation

Slide1

Chapter 3 MESSY

Mansoureh

RoustaSlide2

USING

ALL AVAILABLE DATA BUT

…

Cost

Inexactitude

We never wanted consider them unavoidable and learn to live with them

Going to big data from smallSlide3

In a world of small data

Reducing errors

Ensuring high quality was a natural and essential impulse

Collected a little information so it must be as accurate ass possible

Scientific try to make their measurement more and more precise. (celestial bodies or microscope)

Analyzing only a limited number of data points means errors may get amplified

Slide4

If one could measure a phenomenon, the

implicit

belief was, one could understand it

.

Later

, measurement was tied

to

the scientific method of observation and explanation: the ability to quantify, record, and present reproducible results. Slide5

By the nineteenth century

France…

developed a system of precisely defined units of measurement to capture space, time, and more, and had begun to get other nations to adopt the same standards

.

It was the apex of the age of

measurement

Just half a century later, in the 1920s, the discoveries of quantum mechanics shattered forever the dream of comprehensive and perfect measurement. Slide6

However

in

many new situations that are cropping up today, allowing for imprecision—for messiness—may be a positive feature, not a shortcomingSlide7

you can also increase messiness by combining different types of information from different sources, which don’t always align perfectly.

Messiness can also refer to the inconsistency of formatting, for which the data needs to be “cleaned” before being processed. Slide8

Suppose we need to measure the temperature in a

vineyard

One temperature sensor

a sensor for every one of the hundreds of

vines

Now suppose we increase the frequency of the sensor

readings

The information will be a bit less accurate, but its great volume makes it worthwhile to forgo strict exactitude.Slide9

in many cases it is more fruitful to tolerate error than it would be to work at preventing

it

For instance, we can accept some messiness in return for

scale

As Forrester, a technology consultancy, puts it, “Sometimes two plus two can equal 3.9, and that is good enough.”

Of

course the data can’t be completely incorrect, but we’re willing to sacrifice a bit of accuracy in return for knowing the general trend.Slide10

Everyone knows how much processing power has increased over the years as predicted by Moore’s Law, which states that the number of transistors on a chip doubles

roughly every

two years.

This continual improvement has made computers faster and memory more

plentiful

BUT…Slide11

Fewer of us know that the performance of the algorithms that drive many of our systems has also increased—in many areas more than the improvement of processors under Moore’s Law.

Many of the gains

to

society from big data, however, happen not so much because of faster chips or better algorithms but because there is more data.Slide12

For example, chess algorithms have changed only slightly in the past few

decades

the way computers learn how to

parse words

as we use them in everyday

speech

Around 2000, Microsoft researchers Michele

Banko

and Eric Brill were looking for a method to improve the grammar checker that is part of the company’s Word

programSlide13

They weren’t sure whether it would be more useful to put their effort into

improving

existing algorithms,

finding

new techniques,

or

adding more

sophisticated features

The results were

astounding.

As more data went in, the performance of all four types of algorithms improved dramatically.

Simple algorithm 75% to 95%

Large data algorithm 86% to 94%Slide14

“These results suggest that we may want to reconsider the

tradeoff between

spending time and money on algorithm development

versus spending

it on

corpus development

,”

Banko

and Brill wrote in one of their research papers on the topic

.Slide15

few years after

Banko

and Brill

…

researchers

at

rival

Google were thinking along similar lines—but at an even larger

scale

Instead of testing algorithms with a billion words, they used a trillion.

Google

did this not to develop a grammar checker but to crack an even more complex nut:

language translationSlide16

So-called machine translation has been a

vision of

computer pioneers since the dawn of computing in the

1940s

The

idea took on a special urgency during the Cold

War

At first, computer scientists

opted for

a combination of grammatical rules and a bilingual

dictionary

An IBM computer translated sixty Russian phrases into English in 1954, using 250 word pairs in the computer’s vocabulary and six rules of grammar. The results were very promisingSlide17

The director of the research program, Leon

Dostert

of Georgetown University, predicted

that…

By 1966 a committee of machine-translation

grandees had

to

admit failure

The problem was harder than they had realized it would

be

In the late 1980s, researchers at IBM had a novel

idea…Slide18

In the 1990s IBM’s

Candide

project used ten years’ worth of Canadian

parliamentary transcripts

published in French and English—about three million sentence

pairs

Suddenly, computer translation got a lot

better

Eventually IBM

pulled the

plug

.Slide19

But less than a decade later, in 2006, Google got into translation, as part of its

mission to

“organize the world’s information and make it universally accessible and useful.”

Instead

of nicely translated pages of text in two languages, Google

availed

itself of a larger but also much messier dataset: the entire global Internet and

more

Its trillion-word

corpus

amounted to 95 billion English sentences, albeit of dubious qualitySlide20

Despite the messiness of the input, Google’s service works the

best

By mid-2012 its dataset covered more than 60

languages

The

reason Google’s translation system works well is not that it has a smarter algorithm. It works well because its creators, like

Banko

and Brill at Microsoft, fed in more data—and not just of high

quality

Google was able to use a dataset

tens of

thousands

of times larger than IBM’s

Candide

because it accepted messinessSlide21

More trumps better

Messiness is difficult to accept for the

conventional sampling

analysts, who for all their lives have focused on preventing and

eradicating messiness

They work hard to reduce error rates when collecting samples, and to test the samples for potential biases before announcing their resultsSlide22

They use multiple error-reducing

strategies

Such strategies are costly to implement

and they are hardly feasible for big

data

Moving into a world of big data will require us to change our thinking about the merits of

exactitude we

no longer need to worry so much about individual data points

biasing

the overall

analysisSlide23

At BP’s Cherry Point Refinery in Blaine, Washington, wireless sensors are installed throughout the plant, forming an invisible mesh that produces vast amounts of data in real time.

The

environment of

intense

heat and electrical machinery might distort

the

readings, resulting in messy data

.

But the huge quantity of information generated from both wired and wireless sensors makes up for those hiccupsSlide24

When the quantity of data is vastly larger and is of a new type, exactitude

in

some cases is no longer the goal

It

bears noting

that messiness is not

inherent

to big data.

Instead

it is a function of the imperfection

of

the tools we use to measure, record, and analyze information.

If

the technology were to somehow become perfect, the problem of

inexactitude would

disappearSlide25

Messiness in action

In many areas of technology and society, we are leaning in favor of more and messy over fewer and

exact

These hierarchical

systems

have always been

imperfect,

as everyone familiar with a library card

catalogueSlide26

For example, in 2011 the photo-sharing site Flickr held more than six billion photos from more than 75 million users. Trying to label each photo according to preset categories would have been useless. Would there really have been one

entitled

“Cats that look like Hitler

”?

Instead, clean taxonomies are being replaced by mechanisms that are messier but also

eminently

more flexible and adaptable to a world that evolves and changesSlide27

When we upload photos to Flickr, we “tag”

them

Many of the Web’s most popular sites

flaunt

their

admiration

for imprecision

over

the pretense of

exactitude For example likes in face book or twitterSlide28

Traditional database engines required data to be highly structured and precise.

Data

wasn’t simply stored; it was broken up into “records” that contained fields. Each field held information of a particular type and length.

For example…Slide29

The most common language for accessing databases has long been SQL, or “structured query language.”

The

very name evokes its rigidity

.

But the big shift in recent years has been toward something called

noSQL

, which doesn’t require a preset record structure to work. It accepts data of varying type and size and allows it to be searched successfully. Slide30

Pat

Helland

, one of the world’s foremost authorities on database

design

“

lossy

”

Processing big data

entails

an inevitable

loss of

informationSlide31

Traditional database design promises to deliver consistent results across time. If you ask for your bank account balance, for

example,

you expect to receive the exact

amount….

While traditional systems would have a delay until all updates are

made

Instead

, accepting messiness is a kind of

solution

Hadoop

, an open-source rival to

Google’s

MapReduce

system

Slide32

typical

data analysis requires an operation called “extract, transfer, and load,” or

ETL

Hadoop

: data

can’t be moved and must be analyzed where it

is

Hadoop’s

output isn’t as precise as that of relational databases: it can’t be trusted to launch a spaceship or to certify bank-account details. But for many less critical tasks, where an ultra-precise answer isn’t needed, it does the trick far faster than the alternativesSlide33

In return for living with messiness, we get tremendously

valuable

services that would be impossible at their scope and scale with traditional methods and tools.

For example

ZestFinance

for loan

According to some estimates only 5 percent of all digital data is “structured”—that is, in a form that fits neatly into a traditional database. Without accepting messiness, the remaining 95 percent of unstructured

data so

web pages and videos, remain darkSlide34

Big

data, with its emphasis on comprehensive datasets and messiness, helps us get closer to reality than did our dependence on small data and accuracySlide35

big

data may require

us

to change, to become more comfortable with disorder and uncertaintySlide36

as

the next chapter will explain, finding associations in data and acting on them may often be good enough