domingo, 27 de outubro de 2013

É a armazenação da "BigData" a nova fonte de Ouro ... ? o NOVO PETRÓLEO ?!

Milhões de pessoas disponibilizam dados pessoais na Internet, que ficam armazenados por empresas como a Google, Facebook, Amazon ., etc.
Existem empresas especializadas neste tipo de marketing que combinam informação pessoal para desenvolverem perfis com objectivos comerciais.
Agora, que os casos de espionagem já chegaram aos dirigentes europeus, esta questão do armazenamento da "BigData" está a levantar novamente questões sobre a sua utilização e os Direitos Individuais de Privacidade  ...Os primeiros passos estão a ser desenvolvidos no Parlamento Europeu, para a formulação de uma lei afim de proteger os dados pessoais e regulamentar a sua utilização. Dados que ficam armazenados eternamente, por exemplo, no Facebook.
Entretanto talvez não saiba, mas você e o seu perfil pessoal já tem o valor comercial, no mínimo, de 1.000 euros ...
Entretanto ficam as referências de alguns livros para os interessados / atentos / alarmados ...
António Sérgio Rosa de Carvalho

"My God, Mae thought," The Circle begins: "It's heaven."

Mae is a twentysomething naif, journeying into the brave new world of a vast info-tech enterprise – the Circle – which has amalgamated the functions of Microsoft, Google, Apple, YouTube, Facebook and Twitter into a unified corporation with seemingly beautiful ideals. Customers buy into the Circle with a single identity, their TruYou, which grants them access to every operation and social connection conceivable in the digital universe.

The Circle by Dave Eggers – review
Dave Eggers is at his fluent best in tackling the notion of privacy in an online age
Alexander Linklater

Google’s London HQ: ‘Eggers's innovative optimism has paused at the frontiers of social media, looking forward to an encroaching nightmare.' Photograph: Penson/ Rex Features
It wasn't long ago that Dave Eggers appeared at the cutting edge of American literature, breaking new ground with his meta-memoir, A Heartbreaking Work of Staggering Genius, while his McSweeney's publishing enterprises spawned magazine offshoots in online, DVD and app formats. His pioneering literacy programmes and human rights themes have kept him as socially engaged and culturally connected as any novelist currently writing. Yet with his new novel, Eggers's innovative optimism appears to have paused at the frontiers of social media, looking forward, not to a world of open potential, but to an encroaching nightmare.

"My God, Mae thought," The Circle begins: "It's heaven."

Mae is a twentysomething naif, journeying into the brave new world of a vast info-tech enterprise – the Circle – which has amalgamated the functions of Microsoft, Google, Apple, YouTube, Facebook and Twitter into a unified corporation with seemingly beautiful ideals. Customers buy into the Circle with a single identity, their TruYou, which grants them access to every operation and social connection conceivable in the digital universe.

Deep within the organisation, an inner circle of bosses – the Gang of 40 (lest readers miss the looming Maoist analogy) – fuses technological and human rights idealism into a vision of perfect democracy, transparency and knowledge; one with which they aim to unite private and public spheres and perfect the operations of government.

The Orwellian references scarcely need spelling out. But the Circle's central slogan – "All that happens must be known" – springs less from political ideology than the kind of callow info-utopianism espoused by Julian Assange (who gets a sardonically ambivalent mention in the novel), or the dreams of social connectivity realised by Mark Zuckerberg (lightly referenced in the Circle's founder, Ty Gospodinov).

Mae rises through the ultra-meritocratic ranks of the company to emerge as its leading promotional light, devising some of the key maxims of its credo: "Secrets are lies"; "Sharing is caring"; "Privacy is theft".

Along the way, she is offered – and ignores – increasingly obvious glimpses of what is wrong with all this. One night, she takes an illicit kayak ride to an uninhabited island, momentarily liberated from her hyper-connected world, only to be forced into a session of self-criticism before the assembled staff of the Circle. On a Steve Jobs-style company platform, Mae confesses to her crime – failing to stream her every private experience for the benefit of the community.

In atonement, she turns her life into a model of relentless visibility and her family's into a version of The Truman Show. An ex-boyfriend provides plodding speeches about the nightmare she is fostering, to which she responds by pursuing him through online networks as he seeks to escape to some last, non-mediated corner of wilderness.

It's not clear whether The Circle is intended as a satire of the present or a dystopian vision of the near future. Eggers's writing is so fluent, his ventriloquism of tech-world dialect so light, his denouement so enjoyably inevitable that you forgive the thin characterisation and implausibility of what is really a clever concept novel. As soon as the novel appeared, corners of cultural chatter-sphere lit up in typical, fissiparous mode: commentators both "smiling" and "frowning" (to borrow Circle terms); some happy to take the ride; others perplexed that an author as hip as Eggers should be conservative on social media; others still mocking the daft idea that the infinitely disputatious internet might be subsumed into any unified programme.

It would be daft to compare today's online corporations, or the current surveillance scare, to the real-world totalitarian forces (Nazi, Stalinist, Maoist) evoked by Nineteen Eighty-Four. But Eggers's novel doesn't demand to be read so weightily. Instead, it's a nicely caricatured vision of hi-tech, soft-touch totalitarianism, a narrative thought experiment in which it's liberal idealism (rather than the fascist or communist kind) that reaches a final solution.

There may be some self-mockery in this. Eggers, in his 40s, is no longer a young innovator. He may in fact represent the last generation of major American writers to have emerged entirely from the old values of print. The Circle may be what social media idealists look like, viewed through the form of the traditional, sceptical novel.

June 10, 2013
Watched by the Web: Surveillance Is Reborn

Google does it. Amazon does it. Walmart does it. And, as news reports last week made clear, the United States government does it.

Does what? Uses “big data” analysis of the swelling flood of data that is being generated and stored about virtually every aspect of our lives to identify patterns of behavior and make correlations and predictive assessments.

Amazon uses customer data to give us recommendations based on our previous purchases. Google uses our search data and other information it collects to sell ads and to fuel a host of other services and products.

The National Security Agency, a news article in The Guardian revealed last week, is collecting the phone records of millions of American customers of Verizon — “indiscriminately and in bulk” and “regardless of whether they are suspected of any wrongdoing” — under a secret court order. Under another surveillance program called Prism, The Guardian and The Washington Post reported, the agency has been collecting data from e-mails, audio and video chats, photos, documents and logins, from leading Internet companies like Microsoft, Yahoo, Google, Facebook and Apple, to track foreign targets.

Why spread such a huge net in search of a handful of terrorist suspects? Why vacuum up data so indiscriminately? “If you’re looking for a needle in the haystack, you need a haystack,” Jeremy Bash, chief of staff to Leon E. Panetta, the former director of the Central Intelligence Agency and defense secretary, said on Friday.

In “Big Data,” their illuminating and very timely book, Viktor Mayer-Schönberger, a professor of Internet governance and regulation at the Oxford Internet Institute at Oxford University, and Kenneth Cukier, the data editor for The Economist, argue that the nature of surveillance has changed.

“In the spirit of Google or Facebook,” they write, “the new thinking is that people are the sum of their social relationships, online interactions and connections with content. In order to fully investigate an individual, analysts need to look at the widest possible penumbra of data that surrounds the person — not just whom they know, but whom those people know too, and so on.”

Mr. Cukier and Mr. Mayer-Schönberger argue that big data analytics are revolutionizing the way we see and process the world — they even compare its consequences to those of the Gutenberg printing press. And in this volume they give readers a fascinating — and sometimes alarming — survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy and even on the way we think. Notions of causality, they say, will increasingly give way to correlation as we try to make sense of patterns.

Data is growing incredibly fast — by one account, it is more than doubling every two years — and the authors of this book argue that as storage costs plummet and algorithms improve, data-crunching techniques, once available only to spy agencies, research labs and gigantic companies, are becoming increasingly democratized.

Big data has given birth to an array of new companies and has helped existing companies boost customer service and find new synergies. Before a hurricane, Walmart learned, sales of Pop-Tarts increased, along with sales of flashlights, and so stores began stocking boxes of Pop-Tarts next to the hurricane supplies “to make life easier for customers” while boosting sales. UPS, the authors report, has fitted its trucks with sensors and GPS so that it can monitor employees, optimize route itineraries and know when to perform preventive vehicle maintenance.

Baseball teams like Billy Beane’s Oakland A’s (immortalized in Michael Lewis’s best-seller “Moneyball”) have embraced new number-crunching approaches to scouting players with remarkable success. The 2012 Obama campaign used sophisticated data analysis to build a formidable political machine for identifying supporters and getting out the vote. And New York City has used data analytics to find new efficiencies in everything from disaster response, to identifying stores selling bootleg cigarettes, to steering overburdened housing inspectors directly to buildings most in need of their attention. In the years to come, Mr. Mayer-Schönberger and Mr. Cukier contend, big data will increasingly become “part of the solution to pressing global problems like addressing climate change, eradicating disease and fostering good governance and economic development.”

There is, of course, a dark side to big data, and the authors provide an astute analysis of the dangers they foresee. Privacy has become much more difficult to protect, especially with old strategies — “individual notice and consent, opting out and anonymization” — losing effectiveness or becoming completely beside the point.

“The ability to capture personal data is often built deep into the tools we use every day, from Web sites to smartphone apps,” the authors write. And given the myriad ways data can be reused, repurposed and sold to other companies, it’s often impossible for users to give informed consent to “innovative secondary uses” that haven’t even been imagined when the data was first collected.

The second danger Mr. Cukier and Mr. Mayer-Schönberger worry about sounds like a scenario from the sci-fi movie “Minority Report,” in which predictions seem so accurate that people can be arrested for crimes before they are committed. In the real near future, the authors suggest, big data analysis (instead of the clairvoyant Pre-Cogs in that movie) may bring about a situation “in which judgments of culpability are based on individualized predictions of future behavior.”

Already, insurance companies and parole boards use predictive analytics to help tabulate risk, and a growing number of places in the United States, the authors of “Big Data” say, employ “predictive policing,” crunching data “to select what streets, groups and individuals to subject to extra scrutiny, simply because an algorithm pointed to them as more likely to commit crime.”

Last week an NBC report noted that in so-called signature drone strikes “the C.I.A. doesn’t necessarily know who it is killing”: in signature strikes “intelligence officers and drone operators kill suspects based on their patterns of behavior — but without positive identification.”

One problem with relying on predictions based on probabilities of behavior, Mr. Mayer-Schönberger and Mr. Cukier argue, is that it can negate “the very idea of the presumption of innocence.”

“If we hold people responsible for predicted future acts, ones they may never commit,” they write, “we also deny that humans have a capacity for moral choice.”

At the same time, they observe, big data exacerbates “a very old problem: relying on the numbers when they are far more fallible than we think.” They point to escalation of the Vietnam War under Robert S. McNamara (who served as secretary of defense to Presidents John F. Kennedy and Lyndon B. Johnson) as a case study in “data analysis gone awry”: a fierce advocate of statistical analysis, McNamara relied on metrics like the body count to measure the progress of the war, even though it became clear that Vietnam was more a war of wills than of territory or numbers.

More recent failures of data analysis include the Wall Street crash of 2008, which was accelerated by hugely complicated trading schemes based upon mathematical algorithms. In his best-selling 2012 book, “The Signal and the Noise,” the statistician Nate Silver, who writes the FiveThirtyEight blog for The New York Times, pointed to failures in areas like earthquake science, finance and biomedical research, arguing that “prediction in the era of Big Data” has not been “going very well” (despite his own successful forecasts in the fields of politics and baseball).

Also, as the computer scientist and musician Jaron Lanier points out in his brilliant new book, “Who Owns the Future?,” there is a huge difference between “scientific big data, like data about galaxy formation, weather or flu outbreaks,” which with lots of hard work can be gathered and mined, and “big data about people,” which, like all things human, remains protean, contradictory and often unreliable.

To their credit, Mr. Cukier and Mr. Mayer-Schönberger recognize the limitations of numbers. Though their book leaves the reader with a keen appreciation of the tools that big data can provide in helping us “quantify and understand the world,” it also warns us about falling prey to the “dictatorship of data.”

“We must guard against overreliance on data,” they write, “rather than repeat the error of Icarus, who adored his technical power of flight but used it improperly and tumbled into the sea.”

Book Review: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die

Hospital captures daily large amounts of data about their customers or patients, suppliers, and operations. Health insurance organizations also have large claims data which called as big data-large pools of data that can be captured, communicated, aggregated, stored, and analyzed [1]. We can analyze big health data to detect signals that is useful for patients and healthcare service management, although the data has quality problems. It is increasingly the case that healthcare innovation and growth could take place with predictions analyzing big data [1].

I introduce the book by referring book description and author's interview with

The author makes the how and why of predictive analytics (aka PA) understandable and captivating in various fields of big data through the book. Although it is not targeted only for researcher or stakeholders in healthcare area, the book would be useful for us because it is targeted from the small to large business owner, entrepreneurs, other PAers and us common folk who want to further understand how computerized data research is analyzed to predict specified outcomes and scenarios.

The book breaks down predictive analytics into seven chapters. Cause and effect charts, illustrations along with a few comics and a glossy centerfold divulge cases of predictions in advertising, finance, healthcare, fraud, insurance, government, employment and personal venues. Some topics discussed explain ways to increase consumer buying, limit bank loan defaulting or paying off, anticipate employees quitting or clients dropping cellphone coverage along with collecting online blogs, social networking and risk information. Each chapter includes sections of "what's predicted" and "what's done about it" to show the correlation of PA and gathered data.

The author explains the art of predicting has five effects that include:

1) A little prediction goes a long way,

2) Data is always predictive,

3) Induction is reasoning from detailed facts to general principles,

4) Ensembles compensate for limitations, and

5) Persuasion can be predictable through outcomes.

Using the predictive models of large corporations such as Target, Hewlett-Packard, Chase Bank, Netflix and Telenor along with John Elder's stock market techniques, Jeopardy!'s Watson computer, Kaggle's competitions, and Obama's second term presidential campaign, we can learn the ins and outs of predicting through collecting and interpreting simple to complex data.

By entrusting computers to make decisions, privacy concerns are bought up, prejudices are determined and effects are manipulated when machine learning becomes the translated voice of data. Artificial intelligence can often limit overlearning, crowdsourcing and correlation pitfalls, but will it be able to always correctly interpret language, emotions and feelings of humans as it influences, persuades and molds us?

With even the book's title been subjected to analysis and written sometimes humorously of the writer's own experience of stolen identity and mockery of his geekness, it is an excellent source to any reader that sees computers overtaking and controlling our every move as we continue to be co-dependent on them as we happily benefit from increased information and understanding, attain higher profits and enjoy an easier lifestyle through such a conglomerate of PA data bytes.

In the book, Siegel explains real-world examples on how organizations are turning big data into meaningful metrics. You have been predicted-by companies, governments, law-enforcement, hospitals and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.

Predicting human behavior, by analyzing accumulated health service data not so interested until now, would fortify healthcare. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.

Siegel assert that predictive analytics is the science that unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Also, he describes that perfect prediction is not possible, but even lousy predictions can be extremely valuable.

In the book, Eric Siegel reveals the power and perils of prediction:

What unique form of mortgage risk Chase Bank predicted before the recession.
Predicting which people will drop out of school, cancel a subscription or get divorced before they are even aware of it themselves.
Why early retirement decreases life expectancy and vegetarians miss fewer flights.
Five reasons organizations predict death, including one health insurance company.
The way United States Bank and European wireless carrier Telenor calculate how to most strongly influence each customer.
How companies ascertain untold, private truths-how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job.
How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free.
What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS,, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia.
Siegel insist that predictive analytics as a truly omnipresent science affects everyone, every day. Although largely unseen, it drives millions of decisions, determining who to call, mail, investigate, incarcerate, set up on a date, or medicate.

Predictive analytics transcends human perception. The final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward-but that can be predicted in advance? Whether you are a consumer of it-or consumed by it-get a handle on the power of Predictive Analytics.

Have you ever heard that those who buy diapers are more likely to buy beer too? How about that when stapler is sold at a retailer, it is a good indicator that a company has hired a new employee? Would you be surprised to learn that those using their credit card to buy a drink in a "drinking establishment" are more likely to miss their credit card payment?

A few other surprising facts readers will find in the book include:

Clinical researchers predict divorce with 90 percent accuracy.
Researchers employ machine learning to predict Hollywood blockbusters and hit songs.
Life insurance companies predict age of death to decide whether to approve and then price a policy application.
The state of Maryland uses predictive models to detect inmates more at risk to be perpetrators or victims of murder.
University of Phoenix predicts which students are at risk of failing a course and then target them with intervention measures.
To fully describe what predictive analytics are and how such data is used, Siegel uses these facts and case studies such as big box retailer, Target, predicting which female customers will have a baby in coming months so that they can market relevant items to the expectant parents. When you go to the grocery store or drug store and you get coupons printed that seem to offer just the products you may be interested in, that is another every day use of PA that the average consumer may encounter.

Siegel writes, "I was at Walgreens a few years ago, and upon checkout, an attractive, colorful coupon spit out of the machine. The product it hawked, pictured for all of my fellow shoppers to see, had the potential to mortify. It was a coupon for Beano, a medication for flatulence." The author had developed lactose intolerance, and the drug store was recommending products that might be compatible with his medical condition. He describes PA as benefiting both the consumer and the organization by empowering it "with an entirely new form of competitive armament."

Siegel spends a lot of the book going through the different predictive models and how the data is extracted and used. This type of data mining is being used in numerous ways, not just for consumerism. He provides examples from family and life, healthcare, crime fighting and fraud detection, staff and employees and human language understanding. While the book answers so many questions about how marketers know so much about consumers, it's not just a book for marketers. It is one of those business books that cross many departments including sales, research and development, human resources, customer service among others.

There are many big data in healthcare area such as health insurance corporations, hospitals, bioinformatics research institutions, and disease management and control institutions. PA would contribute to intervene in utilization of healthcare service, guide new way of supply of healthcare service, enhance lifestyle, and prolong life expectancy. The book introduces 147 examples of predictive analysis including healthcare industry. These mini-case studies easily will inspire and broaden understanding and utilization of predictive analytics to health professionals and scholars interested with big data analysis.

Clinical data has issues on ownership of medical records and also legal issues related to data access, pooling, and use [2]. These issues may be huddle to apply predictive science with pooled big data, but we can utilize predictive analytics effectively to the limited data.

Practitioners in hospital can use the book as a guide to invent new way of service and business by using the amount of refuse data. Researchers also may acquire insight on research directions to predictive analysis. Big data aligned from various sources will give meaningful implications to policy maker and practitioners about one's health behavior as the case diaper.

1. Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, et al. Big data: the next frontier for innovation, competition, and productivity. New York (NY): McKinsey Global Institute; 2011.
2. Goodby AW, Olsen L, McGinnis M. Institute of Medicine (US) Roundtable on Value & Science-Driven Health Care. Clinical data as the basic staple of health learning: creating and protecting a public good. Washington (DC): National Academies Press; 2010. [PubMed]

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