Big data is typically broken down by three characteristics, including volume (how much data), velocity (how fast that data is processed), and variety (the various types of data).
Found in: Hurwitz, J., Nugent, A., Halper, F. & Kaufman, M. (2013) Big Data For Dummies. Hoboken, New Jersey, United States of America: For Dummies. ISBN: 9781118504222.
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Industry 4.0. Current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of things and cloud computing Industry 4.0 is the subset of the fourth industrial revolution[1] that concerns industry. The fourth industrial revolution encompasses areas which are not normally classified as industry, such as smart cities, for instance.
Although the terms "industry 4.0" and "fourth industrial revolution" are often used interchangeably, "industry 4.0" factories have machines which are augmented with wireless connectivity and sensors, connected to a system that can visualise the entire production line and make decisions on its own. In essence, industry 4.0 is the trend towards automation and data exchange in manufacturing technologies and processes which include cyber-physical systems (CPS), the internet of things (IoT), industrial internet of things (IIOT)[2], cloud computing [3][4][5][6], cognitive computing and artificial intelligence. 1. 2. 3.
Dataism. Ideology created by big data In 2015, Steve Lohr's book Data-ism looked at how Big Data is transforming society, using the term to describe the Big Data revolution.[4][5] In his 2016 book Homo Deus: A Brief History of Tomorrow, Yuval Noah Harari argues that all competing political or social structures can be seen as data processing systems: "Dataism declares that the universe consists of data flows, and the value of any phenomenon or entity is determined by its contribution to data processing" and "we may interpret the entire human species as a single data processing system, with individual humans serving as its chips. "[2][6] According to Harari, a Dataist should want to "maximise dataflow by connecting to more and more media". Dataism responds to the intricacy and interconnectedness of modern social, economic, and technological realms, which exceed individual understanding.
Big Data History.
◆ Big Data. [org] Big Data. [db] Big Data. [J] Big Data. [C] Big Data. [B] Big Data. Big Data Events in London. A Brief History of Big Data. Big Data has been described by some Data Management pundits (with a bit of a snicker) as “huge, overwhelming, and uncontrollable amounts of information.” In 1663, John Graunt dealt with “overwhelming amounts of information” as well, while he studied the bubonic plague, which was currently ravaging Europe. Graunt used statistics and is credited with being the first person to use statistical data analysis. In the early 1800s, the field of statistics expanded to include collecting and analyzing data. The evolution of Big Data includes a number of preliminary steps for its foundation, and while looking back to 1663 isn’t necessary for the growth of data volumes today, the point remains that “Big Data” is a relative term depending on who is discussing it.
Big Data to Amazon or Google is very different than Big Data to a medium-sized insurance organization, but no less “Big” in the minds of those contending with it. The Foundations of Big Data Data became a problem for the U.S. Big Data Storage. Brief History of Big Data. A Short History Of Big Data. A Very Short History Of Big Data. The story of how data became big starts many years before the current buzz around big data.
Already seventy years ago we encounter the first attempts to quantify the growth rate in the volume of data or what has popularly been known as the “information explosion” (a term first used in 1941, according to the Oxford English Dictionary). The following are the major milestones in the history of sizing data volumes plus other “firsts” in the evolution of the idea of “big data” and observations pertaining to data or information explosion. Last Update: December 21, 2013 1944 Fremont Rider, Wesleyan University Librarian, publishes The Scholar and the Future of the Research Library. He estimates that American university libraries were doubling in size every sixteen years.
Given this growth rate, Rider speculates that the Yale Library in 2040 will have “approximately 200,000,000 volumes, which will occupy over 6,000 miles of shelves… [requiring] a cataloging staff of over six thousand persons.” Spatio-Temporal Analytics and Big Data Mining MSc | UCL Department of Civil, Environmental and Geomatic Engineering. Who coined the term Big Data? | Technology Trend Analysis. Does having more data allow you to make better decision? | Technology Trend Analysis. 2013: Why you are not likely to come across many Big Data success stories | Technology Trend Analysis.
What is the Definition of Big Data? | Technology Trend Analysis. Big Data – Is it a solution in search of a problem? | Technology Trend Analysis. Big Data Network - Economic and Social Research Council. The Power of Scientific Mapping and Visualization: an interview with Prof. Katy Börner - Research Trends. 2016 - (Lambrecht & Tucker) The limits of big data’s competitive edge. BIG DATA: IMPLICATION FOR RESEARCH ON ORGANIZATIONS AND TECHNOLOGY – Organizational Communication & Information Systems. Anaheim, California Divinus Oppong-Tawiah, OCIS Student Rep. Introduction Big data is growing in importance for organizational research, prompting the OCIS Division to sponsor a PDW on Big Data at the 2016 Academy of Management Meeting in Anaheim, California.
Welcoming participants, incoming OCIS Division Chair Mary Beth Watson-Manheim explained that OCIS Executive committee explored different PDW topics and settled on Big Data as potentially affecting many different research areas in OCIS and the larger AOM membership. The committee was thus pleased to have been able to assemble an outstanding group of experts to discuss Big Data from different perspectives focusing on implications for research on organizations and technology, including the opening up new research areas and methods, as well as funding opportunities and ethical dilemmas involved.
Summary of Prof. Alex (Sandy) Pentland’s Keynote Address: In his keynote address, Prof. Panel: Prof. Prof. Panel: Dr. Dr. Panel: Dr. Smart cities need thick data, not big data | Science. Residents living around Plaça del Sol joke that theirs is the only square where, despite the name, rain is preferable. Rain means fewer people gather to socialise and drink, reducing noise for the flats overlooking the square. Residents know this with considerable precision because they’ve developed a digital platform for measuring noise levels and mobilising action.
I was told the joke by Remei, one of the residents who, with her ‘citizen scientist’ neighbours, are challenging assumptions about Big Data and the Smart City. The Smart City and data sovereignty The Smart City is an alluring prospect for many city leaders. Barcelona has been a pioneering Smart City. On the surface, the noise project in Plaça del Sol is an example of such sovereignty. Community developments Plaça de Sol has always been a meeting place. What made Plaça del Sol stand out can be traced to a group of technology activists who got in touch with residents early in 2017. Thick data Attention turned to solutions. The Evolution of Big Data as a Research and Scientific Topic: Overview of the Literature - Research Trends. The use of Big Datasets in bibliometric research - Research Trends. 7 Big Data Trends for 2014. Is “Big Data” racist? Why policing by data isn’t necessarily objective. The following is an excerpt from Andrew Ferguson's 2017 book, The Rise of Big Data Policing and has been re-printed with his permission.
Ferguson is a law professor at the University of the District of Columbia's David A. Clarke School of Law. The rise of big data policing rests in part on the belief that data-based decisions can be more objective, fair, and accurate than traditional policing. Data is data and thus, the thinking goes, not subject to the same subjective errors as human decision making. But in truth, algorithms encode both error and bias. As David Vladeck, the former director of the Bureau of Consumer Protection at the Federal Trade Commission (who was, thus, in charge of much of the law surrounding big data consumer protection), once warned, "Algorithms may also be imperfect decisional tools.
Algorithms themselves are designed by humans, leaving open the possibility that unrecognized human bias may taint the process. Algorithms can also just get it wrong. Big Data: Science Metrics and the black box of Science Policy - Research Trends. Calling all teens: Become a data detective. Just by living our plugged-in lives, each of us is producing a constant stream of data. Little snippets are left behind of what we search, what we buy, where we go, what we tweet … This endless flow of numbers is referred to as “big data,” data sets so large that they require sophisticated parsing to give them meaning. But big data has the potential to tell us a lot about ourselves — unearthing patterns in information flow, energy consumption, weather patterns, disease spread, education trends, and more.
At first glance, big data may not sound like a topic for teenagers. But TED speaker Rick Smolan is on a mission to make it not just accessible but fun. Smolan — who held a conference called The Human Face of Big Data in New York City a few weeks ago featuring TED alums Juan Enriquez, Deb Roy, Esther Dyson, Aaron Koblin and Jer Thorp — is asking students between the ages of 13 and 18 to become “Data Detectives.” For anyone in the age range, becoming a Data Detective is easy. Calling all teens: Become a data detective. 53% Of Companies Are Adopting Big Data Analytics. These and many other insights are from Dresner Advisory Services’ insightful 2017 Big Data Analytics Market Study (94 pp., PDF, client accessed reqd), which is part of their Wisdom of Crowds® series of research.
This 3rd annual report examines end-user trends and intentions surrounding big data analytics, defined as systems that enable end-user access to and analysis of data contained and managed within the Hadoop ecosystem. The 2017 Big Data Analytics Market Study represents a cross-section of data that spans geographies, functions, organization size, and vertical industries. Please see page 10 of the study for additional details regarding the methodology. “Across the three years of our comprehensive study of big data analytics, we see a significant increase in uptake in usage and a large drop of those with no plans to adopt,” said Howard Dresner, founder and chief research officer at Dresner Advisory Services.
Key takeaways include the following: The 10 Vs of Big Data | Transforming Data with Intelligence. The 10 Vs of Big Data Big data goes beyond volume, variety, and velocity alone. You need to know these 10 characteristics and properties of big data to prepare for both the challenges and advantages of big data initiatives. By George FiricanFebruary 8, 2017 The term big data started to show up sparingly in the early 1990s, and its prevalence and importance increased exponentially as years passed. Big data has specific characteristics and properties that can help you understand both the challenges and advantages of big data initiatives. You may have heard of the three Vs of big data, but I believe there are seven additional important characteristics you need to know. . #1: Volume Volume is probably the best known characteristic of big data; this is no surprise, considering more than 90 percent of all today's data was created in the past couple of years. -- 300 hours of video are uploaded to YouTube every minute. -- In 2016 estimated global mobile traffic amounted for 6.2 exabytes per month.
Stop using the term “big data” Many think it’s an important concept, and some think it’s revolutionary, but almost everybody wishes for a better, more descriptive name for it. ONE finding has been common in all the research I’ve done on big data over the last few years: Nobody likes the term. Many think it’s an important concept, and some think it’s revolutionary, but almost everybody wishes for a better, more descriptive name for it. Some managers are objecting only to the hype around the big data buzzword, but I believe many executives simply yearn for a better way to communicate what they are doing with data and analytics. Part of the problem is that “big data” just doesn’t describe the phenomenon very effectively. The term “big” is obviously relative—what’s big today won’t be so large tomorrow. For these reasons, and perhaps others, the term just doesn't suit. So why don’t we simply stop using it? What to call this phenomenon probably isn't your company’s most important problem now.
. © 2018. Ulster University - Fostering the next generation of computing and data experts - Study International. Ulster University encapsulates the spirit of Northern Ireland; it’s an innovative university that fast becomes a second-home for students, who benefit from a first-class education and state-of-the-art facilities. Set against the backdrop of awe-inspiring scenery, including countless Game of Thrones locations, students here experience the very best of both worlds, with an electric social scene to accompany some of the world’s most beautiful sights.
The Faculty of Computing, Engineering and the Built Environment spearheads the innovative nature of Ulster, offering cutting-edge research at the forefront of the computing and data analysis industry. A series of strong industry partnerships in both teaching and research has also helped nurture excellent employment opportunities for graduates. With new, sophisticated algorithms, and complex analytics, many companies are not capturing the full potential value from data because they don’t have the required expertise. Smart cities need thick data, not big data | Science. Is “Big Data” racist? Why policing by data isn’t necessarily objective. 53% Of Companies Are Adopting Big Data Analytics. Big-Data Disruption Gets Real for Car Insurers as O2 Expands. Auto insurance may be on the brink of an incursion from mobile-phone and technology companies. Telefonica SA’s O2 unit -- one of the first mobile operators in Britain to offer car insurance -- expanded its product line in February to include telematics boxes, which track people’s driving habits and can lead to cheaper premiums for youngsters.
That’s stoking speculation that a wave of fintech companies will push into the market and disrupt the way insurers interact with customers. Cell-phone or Internet companies could, for example, use the massive amount of data they hold on their customers to sell them car insurance, bypassing traditional brokers and price-comparison websites. Alphabet Inc.’s Google entry into price comparisons last year failed, but analysts say the web giant could come back and have another go.
“No doubt Google will figure out a way to come back,” said Christopher Ling, a regional leader at Capgemini SA’s insurance practice for the U.K. and Europe in London. U.K. The 10 Vs of Big Data | Transforming Data with Intelligence. Data mining techniques for road safety policy making at University of Birmingham on FindAPhD.com. 2016-11-29 - How India is leading the smart city revolution. The age of the smart city is dawning and for many this creates images of a highly automated and digitised metropolis in the rich countries of the developed world. Earlier this year, Juniper Research crowned Singapore as the world’s smartest city, using a range of factors including adoption of smart grid technologies, intelligent lighting, use of information technology to improve traffic flow, wi-fi access points, smartphone penetration and app availability. Barcelona, London, San Francisco and Oslo took the other top-five spots. “Traditional smart city initiatives require lots of expensive sensors and a lot of data from different sources to create automation,” says Priya Prakash, founder of Design for Social Change, a London-based company that provides smart city technology solutions.
But the concept is being applied across the world, with many of the most ambitious smart city initiatives taking place in Asia. Indian inspiration Putting citizens first The digital divide. How India is leading the smart city revolution. The age of the smart city is dawning and for many this creates images of a highly automated and digitised metropolis in the rich countries of the developed world. Earlier this year, Juniper Research crowned Singapore as the world’s smartest city, using a range of factors including adoption of smart grid technologies, intelligent lighting, use of information technology to improve traffic flow, wi-fi access points, smartphone penetration and app availability. Barcelona, London, San Francisco and Oslo took the other top-five spots. “Traditional smart city initiatives require lots of expensive sensors and a lot of data from different sources to create automation,” says Priya Prakash, founder of Design for Social Change, a London-based company that provides smart city technology solutions.
But the concept is being applied across the world, with many of the most ambitious smart city initiatives taking place in Asia. Indian inspiration Putting citizens first The digital divide. 2016-02-23 - HSCIC plans big data centre of excellence. BIG DATA: IMPLICATION FOR RESEARCH ON ORGANIZATIONS AND TECHNOLOGY – Organizational Communication & Information Systems. Big Data Network - Economic and Social Research Council. Stop using the term “big data” 2014-04-24 - Big Data Week means big business for Northern Ireland | Northern Ireland Science Park.
Who coined the term Big Data? | Technology Trend Analysis. Does having more data allow you to make better decision? | Technology Trend Analysis. 2013: Why you are not likely to come across many Big Data success stories | Technology Trend Analysis. 7 Big Data Trends for 2014. 2013-11-20 - (Hunt) Measurement Without Experimentation is Vanity.
2013-10 - (FMI Quarterly) The New Watson: KM in the Age of Big Data. 2013-06-28 - (Garcia) KM & Innovation - Can Big Learning Data bridge the gap. 2013-07-01 - (Garcia) KM pt2 - Can Big Learning Data bridge the gap. 2013-05-22 - Big Data, for better or worse: 90% of world's data generated over last two years. 2013-05-03 - Google Now, Anticipatory Systems, and the Future of Big Data. 2013-10-14 - Ulster Partners in £7m Project To Develop 'Big Data' Research Centre for NI. 2013-10-11 - Queen's University Belfast named as £7m 'big data' centre in UK-wide research programme. 2013-10-14 - University of Ulster to Open Big Data Research Centre. 2013-10-14 - New program sees big data used for local policing.
2013-08-20 - How 'Big Data' is helping law enforcement. 2013-04-03 - Could big data help the police predict crime? 2013 - Predictive Policing & Big Data. 2012-09-20 - (WSJ) Meet the New Boss: Big Data. 2013-03-10 - (WSJ) How Big Data Is Changing the Whole Equation for Business. What is the Definition of Big Data? | Technology Trend Analysis. Big Data – Is it a solution in search of a problem? | Technology Trend Analysis. 2012-09-30 - Research Trends (Issue30) 2012-01-26 - The Power of Scientific Mapping and Visualization: an interview with Prof. Katy Börner. The Evolution of Big Data as a Research and Scientific Topic. A Big Data Approach to the Humanities, Arts, and Social Sciences: Wikipedia’s View of the World through Supercomputing. The use of Big Datasets in bibliometric research. Big Data: Science Metrics and the black box of Science Policy. Calling all teens: Become a data detective. 2012-12-01 - (Agnihotri) Big Data - the next stop for KM. 2012-10-04 - (Johnson) Big Data & KM.
2012-07-01 - (McGuire) Why Big Data is the New Competitive Advantage. 2012-01-14 - Deja VVVu: Others Claiming Gartner’s Construct for Big Data. 2012 - (Johnson) Big data….. needs big KM. 2012-03-31 - (Lamont) Big data has big implications for KM. 2012-04-29 - (WSJ) Big Data's Big Problem: Little Talent. 2011-02-16 - (Gilenson) Big Data For IT? Get A Handle On Small Data First!