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

Xiuzhen Huang, Steven F. Jennings, Barry Bruce, Alison Buchan, Liming Cai, Pengyin Chen, Carole L. Cramer, Weihua Guan, Uwe K.K. Hilgert, Hongmei Jiang, Zenglu Li, Gail McClure, Donald F. McMullen, Bindu Nanduri, Andy Perkins, Bhanu Rekepalli, Saeed Salem, Jennifer Specker, Karl Walker, Donald WunschDonghai Xiong, Shuzhong Zhang, Yu Zhang, Zhongming Zhao, Jason H. Moore

Research output: Contribution to journalReview article

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number7
JournalBioData Mining
Volume8
Issue number1
DOIs
StatePublished - Jan 8 2015

Keywords

  • Big data
  • Maginot Line
  • No-Boundary thinking

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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