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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