Multiple science data-oriented Technology Roadmapping method

Zhang, Y; Chen, H; Zhang, G; Zhu, D; Lu, J

Multiple science data-oriented Technology Roadmapping method

Zhang, Y

Chen, H

Zhang, G

Zhu, D Lu, J

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Publication Type:: Conference Proceeding
Citation:: Portland International Conference on Management of Engineering and Technology, 2015, 2015-September pp. 2278 - 2287
Issue Date:: 2015-09-21

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Field	Value	Language
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301	en_US
dc.contributor.author	Chen, H https://orcid.org/0000-0002-0893-1817	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Zhu, D	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2015-09-21	en_US
dc.identifier.citation	Portland International Conference on Management of Engineering and Technology, 2015, 2015-September pp. 2278 - 2287	en_US
dc.identifier.isbn	9781890843328	en_US
dc.identifier.uri	http://hdl.handle.net/10453/36798
dc.description.abstract	© 2014 Portland International Conference on Management of Engineering and Technology. 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.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140101366
dc.relation.ispartof	Portland International Conference on Management of Engineering and Technology	en_US
dc.relation.isbasedon	10.1109/PICMET.2015.7273099	en_US
dc.title	Multiple science data-oriented Technology Roadmapping method	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2015-September	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Portland, USA
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2015-September	en_US

Abstract:

© 2014 Portland International Conference on Management of Engineering and Technology. 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.

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http://hdl.handle.net/10453/36798