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Data storage in big data context a survey

Introduction and overview of responses We swim in a sea of data … and the sea level is rising rapidly. Tens of millions of connected people, billions of sensors, trillions of transactions now work to create unimaginable amounts of information. The projected growth of data from all kinds of sources is staggering—to the point where some worry that in the foreseeable future our digital systems of storage and dissemination will not be able to keep up with the simple act of finding places to keep the data and move it around to all those who are interested in it.

OSTP wants to use the technology to accelerate discovery in science and engineering fields and improve national security and education, the White House said. How could Big Data be significant? A 2011 industry report by global management consulting firm McKinsey argued that five new kinds of value might come from abundant data: As the Economist reported: Not only do the advocates worry about profiling, they also worry that those who crunch Big Data with algorithms might draw the wrong conclusions about who someone is, how she might behave in the future, and how to apply the correlations that will emerge in the data analysis.

There are data storage in big data context a survey plenty of technical problems. Getting it into shape for analysis is no tiny task.


Imagine where we might be in 2020. One sketched out a relatively positive future where Big Data are drawn together in ways that will improve social, political, and economic intelligence.

The other expressed the view that Big Data could cause more problems than it solves between now and 2020. Respondents to our query rendered a decidedly split verdict.

Overall, the rise of Big Data is a huge positive for society in nearly all respects. The existence of huge data sets for analysis will engender false confidence in our predictive powers and will lead many to make significant and hurtful mistakes. Moreover, analysis of Big Data will be misused by powerful people and institutions with selfish agendas who manipulate findings to make the case for what they want.

And the advent of Big Data has a harmful impact because it serves the majority at times inaccurately while diminishing the minority and ignoring important outliers.

Overall, the rise of Big Data is a big negative for data storage in big data context a survey in nearly all respects. Respondents were not allowed to select both scenarios; the question was framed this way in order to encourage a spirited and deeply considered written elaboration about the potential of a future with unimaginable amounts of data available to people and organizations.

While about half agreed with the statement that Big Data will yield a positive future, many who chose that view observed that this choice is their hope more than their prediction. A significant number of the survey participants said while they chose the positive or the negative result they expect the true outcome in 2020 will be a little bit of both scenarios. What are the positives, negatives, and shades of grey in the likely future you anticipate?

How will use of Big Data change analysis of the world, change the way business decisions are made, change the way that people are understood? Those who see mostly positives for the future of Big Data share the upside By 2020, the use of Big Data will improve our understanding of ourselves and the world.

Demonizing data, big or small, is demonizing knowledge, and that is never wise. Hal Varian, chief economist at Google, wrote: Nearly every large company has a real-time data warehouse and has more timely data on the economy than our government agencies.

Big Data and Clouds: Research Presentations at IGARSS

This is likely to lead to a better informed, more pro-active fiscal and monetary policy. This will clearly show us interdependence and connections that will lead to a new way of looking at everything. What we buy, eat, donate, and throw away will be visual in a real-time map to see the ripple effect of our actions. That could only lead to mores-conscious behavior. Science fiction gives us plenty of templates for imagining where that will go.

But that dichotomy gets us nowhere. What will be interesting is how social dynamics, economic exchange, and information access are inflected in new ways that open up possibilities that we cannot yet imagine. This will mean a loss of some aspects of society that we appreciate but also usher in new possibilities. It will also contribute to new kinds of crime. A re they enough?

There are difficult matters that must be addressed, which will take time and support, including: As the cost of connectivity goes down the number of these points will go up, diffusing intelligence everywhere. The biggest obstacles to success are technological and behavioral; we need a rapid conversion to IPv6, and we need cooperation among all stakeholders to make the Internet of Things work.

We also need global standards, not just US standards and practices, which draw practical and effective lines about how such a data trove may and may not be used consistent with human rights. Big Data will yield some successes and a lot of failures, and most people will continue merely to muddle along, hoping not to be mugged too frequently by the well-intentioned or not entrepreneurs and bureaucrats who delight in trying to use this shiny new toy to fix the world.

Statistics can still lie. Barnes, visiting professor at Guangxi University in China. Without those curators the data will become more and more plentiful, more overwhelming and [it will] confuse our political and social conversations by an overabundance of numbers that can make any point we want to make them make.

Big Data has the potential for significant negative impacts that may data storage in big data context a survey impossible to avoid. The general public will not understand the underlying conflicts and will naively trust the output. This is already happening and will only get worse as Big Data continues to evolve.

How to Lie with the Internet of Things becomes an underground bestseller. Manipulation and surveillance are at the heart of their Big Data agendas. National security apparatus and ever-more-focused marketing including political databases. Neither of these are intended for the benefit of individual network users but rather look at users as either potential terrorists or as buyers of goods and services.

The Future of Big Data

The end result will, in most cases, be more effective targeting of people with the goal of having them consume more goods, which I believe is a negative for society. I would not call that misuse, but I would call it a self-serving agenda.

While many can be used in constructive, positive ways to improve life and services for many, Big Data will predominantly be used to feed people ads based on their behavior and friends, to analyze data storage in big data context a survey potential for health and other forms of insurance, and to essentially compartmentalize people and expose them more intensely to fewer and fewer things.

And yes, I know about farmers in Africa using their cell phones to track prices for produce in the big cities. The grassroots boom in bottom-up innovation will increasingly find new ways to self-organize as evidenced in 2011 by the Occupy Wall Street and Arab Spring movements. Maybe trust features can be built in.

And then be prepared to misjudge sometimes. Barry Parr, owner and analyst for MediaSavvy, contributed this thought: Humans seem to think they know more than they actually know. Still, despite all of our flaws, this new way of looking at the big picture could help.

One version of this kind of summary thought was written by Stowe Boyd, principal at Stowe Boyd and The Messengers, a research, consulting, and media business based in New York City: And there will be dark episodes, too, since the brightest light casts the darkest shadow.

There are opportunities for terrible applications, like the growth of the surveillance society, where the authorities watch everything and analyze our actions, behavior, and movements looking for patterns of illegality, something like a real-time Minority Report.

On the other side, access to more large data can also be a blessing, so social advocacy groups may be able to amass information at a low- or zero-cost that would be unaffordable today.

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For example, consider the bottom-up creation of an alternative food system, outside the control of multinational agribusiness, and connecting local and regional food producers and consumers.

So it will be a mixed bag, like most human technological advances. The view expressed by Jerry Michalski, founder and president of Sociate and consultant for the Institute for the Future, weaves in the good, bad, and in between in a practical way: Humans consistently seem to think they know more than they actually know in retrospect.

Our understanding of technological effects, for example, lags by many decades the inexorable effects of implementation. So the best-intentioned of humans will try to use Big Data to solve Big Problems, but are unlikely to do well at it.

Big Ideas have driven innumerable bad decisions over time. These all have led us into mess after mess. Meanwhile, the worst-intentioned will have at hand immensely powerful ways to do harm, from hidden manipulation of the population to all sorts of privacy invasions.

Also, data coming out of fMRI experiments will convince us we know how people make decisions, leading to more mistaken policies. There are a few bright spots on the horizon. When crowds of people work openly with one another around real data, they can make real progress.

We need small groups empowered by Big Data, then coordinating with other small groups everywhere to find what works pragmatically. Computer science, data-mining, and a growing network of sensors and information-collection software programs are giving rise to a phenomenal occurrence, the knowable future.

The rate by which we can predict aspects of the future is quickening as rapidly as is the spread of the Internet, because the two are inexorably linked. The Internet is turning prediction into an equation. Computer-aided prediction comes in a wide variety of forms and guises, from AI programs that chart potential flu outbreaks to expensive yet imperfect quant algorithms that anticipate outbreaks of stock market volatility.

But the basic process is not dramatically different from what plays out when the human brain makes a prediction. These systems analyze sensed data in the context of stored information to extrapolate a pattern the same way the early earthquake warning system used its network of sensors to detect the P wave and thus project the S wave. What differs between these systems, between humans predictors and machine predictors, is the sensing tools. Humans are limited to two eyes, two ears, and a network of nerve endings.

Computers can sense via a much wider menagerie of data collection tools. Many firms have gotten a lot better at predicting human patterns using those sense tools. Some, like Google, are data storage in big data context a survey household names.

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In the coming years, Google is going to leverage the massive amount of user data that it collects on a minute by minute to basis to extrapolate trends in human activity and thus predict future activity. Google has been doing this with some success in terms of flu for several years now with its popular Flu Trends program.

It works exactly how you would imagine that it would. We have found a close relationship between how many people search for flu-related topics and how many people actually have flu symptoms, says Google on its Flu Trends Web site.

As Nicholas Christakis described in his book Connected querying activity and social network activity can reveal infectious disease trends long before data on those trends is released to the public by prudent government agencies.

But does the same phenomenon, i. Consider that in 2010 two Notre Dame researchers, Zhi Da and Penhji Paul Gao, showed that querying activity around particular companies can, somewhat reliably, predict a stock price increase for those companies. Better personalization in terms of display ads is a function of prediction.