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Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm

Whether your interests lie in scientific arenas, the corporate world, or in government, you have certainly heard the praises of big data: Big data will give you new insights, allow you to become more efficient, and/or will solve your problems. While big data has had some outstanding successes, many...

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Autores principales: Huang, Xiuzhen, Jennings, Steven F, Bruce, Barry, Buchan, Alison, Cai, Liming, Chen, Pengyin, Cramer, Carole L, Guan, Weihua, Hilgert, Uwe KK, Jiang, Hongmei, Li, Zenglu, McClure, Gail, McMullen, Donald F, Nanduri, Bindu, Perkins, Andy, Rekepalli, Bhanu, Salem, Saeed, Specker, Jennifer, Walker, Karl, Wunsch, Donald, Xiong, Donghai, Zhang, Shuzhong, Zhang, Yu, Zhao, Zhongming, Moore, Jason H
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4323225/
https://www.ncbi.nlm.nih.gov/pubmed/25670967
http://dx.doi.org/10.1186/s13040-015-0037-5
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author Huang, Xiuzhen
Jennings, Steven F
Bruce, Barry
Buchan, Alison
Cai, Liming
Chen, Pengyin
Cramer, Carole L
Guan, Weihua
Hilgert, Uwe KK
Jiang, Hongmei
Li, Zenglu
McClure, Gail
McMullen, Donald F
Nanduri, Bindu
Perkins, Andy
Rekepalli, Bhanu
Salem, Saeed
Specker, Jennifer
Walker, Karl
Wunsch, Donald
Xiong, Donghai
Zhang, Shuzhong
Zhang, Yu
Zhao, Zhongming
Moore, Jason H
author_facet Huang, Xiuzhen
Jennings, Steven F
Bruce, Barry
Buchan, Alison
Cai, Liming
Chen, Pengyin
Cramer, Carole L
Guan, Weihua
Hilgert, Uwe KK
Jiang, Hongmei
Li, Zenglu
McClure, Gail
McMullen, Donald F
Nanduri, Bindu
Perkins, Andy
Rekepalli, Bhanu
Salem, Saeed
Specker, Jennifer
Walker, Karl
Wunsch, Donald
Xiong, Donghai
Zhang, Shuzhong
Zhang, Yu
Zhao, Zhongming
Moore, Jason H
author_sort Huang, Xiuzhen
collection PubMed
description Whether your interests lie in scientific arenas, the corporate world, or in government, you have certainly heard the praises of big data: Big data will give you new insights, allow you to become more efficient, and/or will solve your problems. While big data has had some outstanding successes, many are now beginning to see that it is not the Silver Bullet that it has been touted to be. Here our main concern is the overall impact of big data; the current manifestation of big data is constructing a Maginot Line in science in the 21st century. Big data is not “lots of data” as a phenomena anymore; The big data paradigm is putting the spirit of the Maginot Line into lots of data. Big data overall is disconnecting researchers and science challenges. We propose No-Boundary Thinking (NBT), applying no-boundary thinking in problem defining to address science challenges.
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spelling pubmed-43232252015-02-11 Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm Huang, Xiuzhen Jennings, Steven F Bruce, Barry Buchan, Alison Cai, Liming Chen, Pengyin Cramer, Carole L Guan, Weihua Hilgert, Uwe KK Jiang, Hongmei Li, Zenglu McClure, Gail McMullen, Donald F Nanduri, Bindu Perkins, Andy Rekepalli, Bhanu Salem, Saeed Specker, Jennifer Walker, Karl Wunsch, Donald Xiong, Donghai Zhang, Shuzhong Zhang, Yu Zhao, Zhongming Moore, Jason H BioData Min Editorial Whether your interests lie in scientific arenas, the corporate world, or in government, you have certainly heard the praises of big data: Big data will give you new insights, allow you to become more efficient, and/or will solve your problems. While big data has had some outstanding successes, many are now beginning to see that it is not the Silver Bullet that it has been touted to be. Here our main concern is the overall impact of big data; the current manifestation of big data is constructing a Maginot Line in science in the 21st century. Big data is not “lots of data” as a phenomena anymore; The big data paradigm is putting the spirit of the Maginot Line into lots of data. Big data overall is disconnecting researchers and science challenges. We propose No-Boundary Thinking (NBT), applying no-boundary thinking in problem defining to address science challenges. BioMed Central 2015-02-06 /pmc/articles/PMC4323225/ /pubmed/25670967 http://dx.doi.org/10.1186/s13040-015-0037-5 Text en © Huang et al.; licensee BioMed Central. 2015 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Editorial
Huang, Xiuzhen
Jennings, Steven F
Bruce, Barry
Buchan, Alison
Cai, Liming
Chen, Pengyin
Cramer, Carole L
Guan, Weihua
Hilgert, Uwe KK
Jiang, Hongmei
Li, Zenglu
McClure, Gail
McMullen, Donald F
Nanduri, Bindu
Perkins, Andy
Rekepalli, Bhanu
Salem, Saeed
Specker, Jennifer
Walker, Karl
Wunsch, Donald
Xiong, Donghai
Zhang, Shuzhong
Zhang, Yu
Zhao, Zhongming
Moore, Jason H
Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm
title Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm
title_full Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm
title_fullStr Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm
title_full_unstemmed Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm
title_short Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm
title_sort big data - a 21st century science maginot line? no-boundary thinking: shifting from the big data paradigm
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4323225/
https://www.ncbi.nlm.nih.gov/pubmed/25670967
http://dx.doi.org/10.1186/s13040-015-0037-5
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