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 Cramer
Weihua Guan
Uwe KK Hilgert
Hongmei Jiang
Zenglu Li
Gail McClure
Donald F. McMullen
Bindu Nanduri
Andy Perkins
Bhanu Rekepalli
Saeed Salem
Jennifer Specker
Karl Walker
Donald C. Wunsch, Missouri University of Science and Technology
Donghai Xiong
Shuzhong Zhang
Yu Zhang
Zhongming Zhao
Jason H. Moore

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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.