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Tag Archive | "big data"

Pew Research Internet Project: Killer Apps in the Gigabit Age

Full Article: Killer Apps in the Gigabit Age
Source: Pew Research Internet Project

Killer Apps in a Gigabit Age: Themes

  • People’s basic interactions and their ability to ‘be together’ and collaborate will change in the age of vivid telepresence—enabling people to instantly ‘meet face-to-face’ in cyberspace with no travel necessary.
  • Augmented reality will extend people’s sense and understanding of their real-life surroundings and virtual reality will make some spaces, such as gaming worlds and other simulated environments, even more compelling places to hang out.
  • The connection between humans and technology will tighten as machines gather, assess, and display real-time personalized information in an ‘always-on’ environment. This integration will affect many activities—including thinking, the documentation of life events (‘life-logging’), and coordination of daily schedules.
  • Specific economic and social sectors will be especially impacted; health/medicine and education were mentioned often.
  • New digital divides may open as people gain opportunities on different timelines and with different tools.
  • Who knows? ‘I have no idea due to rapid change.’ ‘The best Internet apps are yet to emerge.’ ‘If I knew, I wouldn’t tell you, I would invest in it!’
  • Advances will be gradual for various reasons: Bandwidth is not the issue. The US will lag because a widespread gigabit network is not easily achieved.

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Data Flood: Helping the Navy Address the Rising Tide of Sensor Information

Data Flood: Helping the Navy Address the Rising Tide of Sensor Information
Source: RAND Corporation

In the U.S. Navy, there is a growing demand for intelligence, surveillance, and reconnaissance (ISR) data, which help Navy commanders obtain situational awareness and help Navy vessels perform a host of mission-critical tasks. The amount of data generated by ISR sensors has, however, become overwhelming, and Navy analysts are struggling to keep pace with this data flood. Their challenges include extremely slow download times, workstations cluttered with applications, and stovepiped databases and networks — challenges that are only going to intensify as the Navy fields new and additional sensors in the coming years. Indeed, if the Navy does not change the way it collects, processes, exploits, and disseminates information, it will reach an ISR “tipping point” — the point at which its analysts are no longer able to complete a minimum number of exploitation tasks within given time constraints — as soon as 2016.

The authors explore options for solving the Navy’s “big data” challenge, considering changes across four dimensions: people, tools and technology, data and data architectures, and demand and demand management. They recommend that the Navy pursue a cloud solution — a strategy similar to those adopted by Google, the Intelligence Community, and other large organizations grappling with big data’s challenges and opportunities.

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Predictive Modeling With Big Data: Is Bigger Really Better?

Predictive Modeling With Big Data: Is Bigger Really Better?
Source: Big Data

With the increasingly widespread collection and processing of “big data,” there is natural interest in using these data assets to improve decision making. One of the best understood ways to use data to improve decision making is via predictive analytics. An important, open question is: to what extent do larger data actually lead to better predictive models? In this article we empirically demonstrate that when predictive models are built from sparse, fine-grained data—such as data on low-level human behavior—we continue to see marginal increases in predictive performance even to very large scale. The empirical results are based on data drawn from nine different predictive modeling applications, from book reviews to banking transactions. This study provides a clear illustration that larger data indeed can be more valuable assets for predictive analytics. This implies that institutions with larger data assets—plus the skill to take advantage of them—potentially can obtain substantial competitive advantage over institutions without such access or skill. Moreover, the results suggest that it is worthwhile for companies with access to such fine-grained data, in the context of a key predictive task, to gather both more data instances and more possible data features. As an additional contribution, we introduce an implementation of the multivariate Bernoulli Naïve Bayes algorithm that can scale to massive, sparse data.

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Occupational Outlook Quarterly: Working with big data

Working with big data
Source: Bureau of Labor Statistics (Occupational Outlook Quarterly)

This year, 2013, is The International Year of Statistics. It’s a designation intended to highlight the role that data and statistical analysis have in society. To further that goal, this article describes work with big data. The first section outlines what big data is. The second section provides an overview of big data work. The third section explains some of the challenges that big data work entails. The fourth section describes how to prepare for this work. Sources of information are provided at the end.

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Big Data, H.P. Lovecraft, and common sense

hplovecraft

“The most merciful thing in the world, I think, is the inability of the human mind to correlate all its contents. We live on a placid island of ignorance in the midst of black seas of infinity, and it was not meant that we should voyage far. The sciences, each straining in its own direction, have hitherto harmed us little; but some day the piecing together of dissociated knowledge will open up such terrifying vistas of reality, and of our frightful position therein, that we shall either go mad from the revelation or flee from the deadly light into the peace and safety of a new dark age.”  — H.P Lovecraft, “Call of Cthulhu,” Weird Tales, 11, No. 2 (February 1928), 159–78, 287.

While it is obvious that H.P. Lovecraft could not know about big data, this quote is very relevant.  Alexis Madrigal of Nextgov’s Big Data blog thinks so as well.  That is where I first saw this quote and its being linked to big data.  There are many things about big data and how it can be used that are very scary.  We have seen recently where is is actually difficult to limit how much data you may end up collecting because of how hard it is to separate out what you need from what is found (ie. NSA and FISA).  It is important to not blame the technology as the problem.  The problem is its application and use by human beings. 

There are many “Big Benefits” from the use and application of “Big Data.”  Look at the growth and maturation of the field of bioinformatics and its use in medicine.   Sequencing of the human genome is the application of big data.  Genomics will change how we are treated for disease. 

Big Data will help us in the battle to overcome global warming.  Increasingly accurate weather forecasts and improved computer models of the effects of global warming are all applications of big data.

Big data is now showing up in all the hard sciences and in the “soft” such the social sciences.  It is impossible to get away from it.

All of us in special libraries, especially in business, technical and research libraries, have seen our jobs change because of interest in big data and because many of us are directly involved in the exploration, analysis, and manipulation of big data sets. 

A bit of common sense will help us avoid us the fate suggested by H.P. Lovecraft of  “mad from the revelation or flee from the deadly light into the peace and safety of a new dark age.”  As with all technologies, big data is not in itself good or bad.  It is in how it is used.  As librarians we can help direct its use into positive directions.

Note: These are my own opinions and not the opinions of SLA, Military Libraries Division of SLA, my employer, or the U.S.Air Force or DoD.  — Bill Drew

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Big Data – a working definition

“BIG DATA IS DATA THAT EXCEEDS THE PROCESSING CAPACITY OF CONVENTIONAL DATABASE SYSTEMS.”

Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it.

Edd Dumbill. Big Data. March 2013, 1(1): 1-2. doi:10.1089/big.2012.1503.

Edd Dumbill
Editor-in-Chief
Big Data

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