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

Statistical Analysis and Data Mining

Jeffrey L. Solka¹  Carey E. Priebe²  David J. Marchette³  Avory C. Bryant³ 

[1] 2682, USA;Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218‐Naval Surface Warfare Center, Dahlgren, VA, USA

关键词: statistical inference;    text processing;    graphs and networks;   

DOI  :  10.1002/sam.11143

学科分类：社会科学、人文和艺术（综合）

来源: John Wiley & Sons, Inc.

【 摘 要 】

Abstract `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 quantita.

【 授权许可】

Unknown