TY - GEN
T1 - Multiple science data-oriented Technology Roadmapping method
AU - Zhang, Yi
AU - Chen, Hongshu
AU - Zhang, Guangquan
AU - Zhu, Donghua
AU - Lu, Jie
N1 - Publisher Copyright:
© 2014 Portland International Conference on Management of Engineering and Technology.
PY - 2015/9/21
Y1 - 2015/9/21
N2 - Since its first engagement with industry decades ago, Technology Roadmapping (TRM) is taking a more and more important role for technical intelligence in current R&D planning and innovation tracking. Important topics for both science policy and engineering management researchers involves with the approaches that refer to the real-world problems, explore value-added information from the complex data sets, fuse the analytic results and expert knowledge effectively and reasonable, and demonstrate to the decision makers visually and understandable. Moreover, the growing variety of science data sources in the Big Data Age increases these challenges and opportunities. Addressing these concerns, this paper proposes a TRM composing method with a clustering-based topic identification model, a multiple science data sources integration model, and a semi-automated fuzzy set-based TRM composing model with expert aid. We focus on a case study on computer science related R&D. Empirical data from the United States National Science Foundation Award data (innovative research ideas and proposals) and Derwent Innovation Index data source (patents emphasizing technical products) provide vantage points at two stages of the R&D process. The understanding gained will assist in description of computer science macro-trends for R&D decision makers.
AB - Since its first engagement with industry decades ago, Technology Roadmapping (TRM) is taking a more and more important role for technical intelligence in current R&D planning and innovation tracking. Important topics for both science policy and engineering management researchers involves with the approaches that refer to the real-world problems, explore value-added information from the complex data sets, fuse the analytic results and expert knowledge effectively and reasonable, and demonstrate to the decision makers visually and understandable. Moreover, the growing variety of science data sources in the Big Data Age increases these challenges and opportunities. Addressing these concerns, this paper proposes a TRM composing method with a clustering-based topic identification model, a multiple science data sources integration model, and a semi-automated fuzzy set-based TRM composing model with expert aid. We focus on a case study on computer science related R&D. Empirical data from the United States National Science Foundation Award data (innovative research ideas and proposals) and Derwent Innovation Index data source (patents emphasizing technical products) provide vantage points at two stages of the R&D process. The understanding gained will assist in description of computer science macro-trends for R&D decision makers.
UR - http://www.scopus.com/inward/record.url?scp=84955576681&partnerID=8YFLogxK
U2 - 10.1109/PICMET.2015.7273099
DO - 10.1109/PICMET.2015.7273099
M3 - Conference contribution
AN - SCOPUS:84955576681
T3 - Portland International Conference on Management of Engineering and Technology
SP - 2278
EP - 2287
BT - PICMET 2015 - Portland International Center for Management of Engineering and Technology
A2 - Daim, Tugrul U.
A2 - Kozanoglu, Dilek Cetindamar
A2 - Kocaoglu, Dundar F.
A2 - Anderson, Timothy R.
A2 - Perman, Gary
A2 - Niwa, Kiyoshi
PB - Portland State University
T2 - Portland International Center for Management of Engineering and Technology, PICMET 2015
Y2 - 2 August 2015 through 6 August 2015
ER -