Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study

Zhang, Y; Huang, Y; Porter, AL; Zhang, G; Lu, J

Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study

Zhang, Y

Huang, Y Porter, AL Zhang, G

Lu, J

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Publication Type:: Journal Article
Citation:: Technological Forecasting and Social Change, 2019, 146 pp. 795 - 807
Issue Date:: 2019-09-01

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Field	Value	Language
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301	en_US
dc.contributor.author	Huang, Y	en_US
dc.contributor.author	Porter, AL	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.available	2020-05-25T19:21:26Z
dc.date.issued	2019-09-01	en_US
dc.identifier.citation	Technological Forecasting and Social Change, 2019, 146 pp. 795 - 807	en_US
dc.identifier.issn	0040-1625	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131068
dc.description.abstract	© 2018 As one of the most impactful emerging technologies, big data analytics and its related applications are powering the development of information technologies and are significantly shaping thinking and behavior in today's interconnected world. Exploring the technological evolution of big data research is an effective way to enhance technology management and create value for research and development strategies for both government and industry. This paper uses a learning-enhanced bibliometric study to discover interactions in big data research by detecting and visualizing its evolutionary pathways. Concentrating on a set of 5840 articles derived from Web of Science covering the period between 2000 and 2015, text mining and bibliometric techniques are combined to profile the hotspots in big data research and its core constituents. A learning process is used to enhance the ability to identify the interactive relationships between topics in sequential time slices, revealing technological evolution and death. The outputs include a landscape of interactions within big data research from 2000 to 2015 with a detailed map of the evolutionary pathways of specific technologies. Empirical insights for related studies in science policy, innovation management, and entrepreneurship are also provided.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP150101645
dc.relation.ispartof	Technological Forecasting and Social Change	en_US
dc.relation.isbasedon	10.1016/j.techfore.2018.06.007	en_US
dc.subject.classification	Science Studies	en_US
dc.title	Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study	en_US
dc.type	Journal Article
utslib.citation.volume	146	en_US
utslib.for	1005 Communications Technologies	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1504 Commercial Services	en_US
utslib.for	10 Technology	en_US
utslib.for	14 Economics	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
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	open_access
pubs.publication-status	Published	en_US
pubs.volume	146	en_US

Abstract:

© 2018 As one of the most impactful emerging technologies, big data analytics and its related applications are powering the development of information technologies and are significantly shaping thinking and behavior in today's interconnected world. Exploring the technological evolution of big data research is an effective way to enhance technology management and create value for research and development strategies for both government and industry. This paper uses a learning-enhanced bibliometric study to discover interactions in big data research by detecting and visualizing its evolutionary pathways. Concentrating on a set of 5840 articles derived from Web of Science covering the period between 2000 and 2015, text mining and bibliometric techniques are combined to profile the hotspots in big data research and its core constituents. A learning process is used to enhance the ability to identify the interactive relationships between topics in sequential time slices, revealing technological evolution and death. The outputs include a landscape of interactions within big data research from 2000 to 2015 with a detailed map of the evolutionary pathways of specific technologies. Empirical insights for related studies in science policy, innovation management, and entrepreneurship are also provided.

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http://hdl.handle.net/10453/131068