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Big data[electronic resource] :techniques and technologies in geoinformatics /

Record Type:	Language materials, printed : Monograph/item
[NT 15000414]:	910.285/57
Title/Author:	Big data : techniques and technologies in geoinformatics // edited by Hassan A. Karimi.
other author:	Karimi, Hassan A.
Published:	Boca Raton : : CRC Press, Taylor & Francis Group,, 2014.
Description:	1 online resource (xiv, 298 p.) : : ill., maps.
Subject:	Geography - Data processing.
Subject:	Big data.
Subject:	Geographic information systems.
Subject:	Geospatial data.
Subject:	High performance computing.
ISBN:	9781466586550 (electronic bk.)
[NT 15000227]:	Includes bibliographical references and index.
[NT 15000229]:	"Preface What is big data? Due to increased interest in this phenomenon, many recent papers and reports have focused on defining and discussing this subject. A review of these publications would point to a consensus about how big data is perceived and explained. It is widely agreed that big data has three specific characteristics: volume, in terms of large-scale data storage and processing; variety, or the availability of data in different types and formats; and velocity, which refers to the fast rate of new data acquisition. These characteristics are widely referred to as the three Vs of big data, and while projects involving datasets that only feature one of these Vs are considered to be big, most datasets from such fields as science, engineering, and social media feature all three Vs. To better understand the recent spurt of interest in big data, I provide here a new and different perspective on it. I argue that the answer to the question of "What is big data?" depends on when the question is asked, what application is involved, and what computing resources are available. In other words, understanding what big data is requires an analysis of time, applications, and resources. In light of this, I categorize the time element into three groups: past (since the introduction of computing several decades ago), near-past (within the last few years), and present (now). One way of looking at the time element is that, in general, big data in the past meant dealing with gigabyte-sized datasets, in the near-past, terabyte-sized datasets, and in the present, petabyte-sized datasets. I also categorize the application element into three groups: scientific (data used for complex modeling, analysis, and simulation), business (data used for business analysis and modeling), and general"--
Online resource:	http://www.crcnetbase.com/isbn/9781466586550

Big data[electronic resource] :techniques and technologies in geoinformatics /
Big datatechniques and technologies in geoinformatics /[electronic resource] :edited by Hassan A. Karimi. - Boca Raton :CRC Press, Taylor & Francis Group,2014. - 1 online resource (xiv, 298 p.) :ill., maps.

Includes bibliographical references and index.

"Preface What is big data? Due to increased interest in this phenomenon, many recent papers and reports have focused on defining and discussing this subject. A review of these publications would point to a consensus about how big data is perceived and explained. It is widely agreed that big data has three specific characteristics: volume, in terms of large-scale data storage and processing; variety, or the availability of data in different types and formats; and velocity, which refers to the fast rate of new data acquisition. These characteristics are widely referred to as the three Vs of big data, and while projects involving datasets that only feature one of these Vs are considered to be big, most datasets from such fields as science, engineering, and social media feature all three Vs. To better understand the recent spurt of interest in big data, I provide here a new and different perspective on it. I argue that the answer to the question of "What is big data?" depends on when the question is asked, what application is involved, and what computing resources are available. In other words, understanding what big data is requires an analysis of time, applications, and resources. In light of this, I categorize the time element into three groups: past (since the introduction of computing several decades ago), near-past (within the last few years), and present (now). One way of looking at the time element is that, in general, big data in the past meant dealing with gigabyte-sized datasets, in the near-past, terabyte-sized datasets, and in the present, petabyte-sized datasets. I also categorize the application element into three groups: scientific (data used for complex modeling, analysis, and simulation), business (data used for business analysis and modeling), and general"--

ISBN: 9781466586550 (electronic bk.)

LCCN: 2013047353Subjects--Topical Terms:

518247
Geography
--Data processing.

LC Class. No.: G70.2 / .B54 2014

Dewey Class. No.: 910.285/57

Big data[electronic resource] :techniques and technologies in geoinformatics /
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245 0 0 $a Big data $h [electronic resource] : $b techniques and technologies in geoinformatics / $c edited by Hassan A. Karimi.
260 $a Boca Raton : $b CRC Press, Taylor & Francis Group, $c 2014.
300 $a 1 online resource (xiv, 298 p.) : $b ill., maps.
504 $a Includes bibliographical references and index.
520 $a "Preface What is big data? Due to increased interest in this phenomenon, many recent papers and reports have focused on defining and discussing this subject. A review of these publications would point to a consensus about how big data is perceived and explained. It is widely agreed that big data has three specific characteristics: volume, in terms of large-scale data storage and processing; variety, or the availability of data in different types and formats; and velocity, which refers to the fast rate of new data acquisition. These characteristics are widely referred to as the three Vs of big data, and while projects involving datasets that only feature one of these Vs are considered to be big, most datasets from such fields as science, engineering, and social media feature all three Vs. To better understand the recent spurt of interest in big data, I provide here a new and different perspective on it. I argue that the answer to the question of "What is big data?" depends on when the question is asked, what application is involved, and what computing resources are available. In other words, understanding what big data is requires an analysis of time, applications, and resources. In light of this, I categorize the time element into three groups: past (since the introduction of computing several decades ago), near-past (within the last few years), and present (now). One way of looking at the time element is that, in general, big data in the past meant dealing with gigabyte-sized datasets, in the near-past, terabyte-sized datasets, and in the present, petabyte-sized datasets. I also categorize the application element into three groups: scientific (data used for complex modeling, analysis, and simulation), business (data used for business analysis and modeling), and general"-- $c Provided by publisher.
588 $a Description based on print version record.
650 0 $a Geography $x Data processing. $3 518247
650 0 $a Big data. $3 571002
650 0 $a Geographic information systems. $3 446321
650 0 $a Geospatial data. $3 467016
650 0 $a High performance computing. $3 386197
700 1 $a Karimi, Hassan A. $3 488091
856 4 0 $u http://www.crcnetbase.com/isbn/9781466586550