Quantitative Horizon Scanning for Mitigating Technological Surprise: Detecting the Potential for Collaboration at the Interface | Zendy

Priebe Carey E. | Zendy; Solka Jeffrey L. | Zendy; Marchette David J. | Zendy; Bryant Avory C. | Zendy

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Quantitative Horizon Scanning for Mitigating Technological Surprise: Detecting the Potential for Collaboration at the Interface

Author(s) -

Priebe Carey E.,

Solka Jeffrey L.,

Marchette David J.,

Bryant Avory C.

Publication year - 2012

Publication title -

statistical analysis and data mining: the asa data science journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.381

H-Index - 33

eISSN - 1932-1872

pISSN - 1932-1864

DOI - 10.1002/sam.11143

Subject(s) - surprise , data science , computer science , identification (biology) , lament , interface (matter) , data mining , sociology , art , botany , literature , communication , bubble , maximum bubble pressure method , parallel computing , biology

‘The identification of potential breakthroughs before they happen’ is a vague data analysis problem and ‘the scientific literature’ is a massive, complex dataset. Hence QHS for MTS might seem to be prototypical of the data miner's lament: ‘Here's some data we have… can you find something interesting?’ Nonetheless, the problem is real and important, and we develop an innovative statistical approach thereto—not a final etched‐in‐stone approach, but perhaps the first complete quantitative methodology explicitly addressing QHS for MTS. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining5: 178–186, 2012

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