IEEE Transactions on
Special Issue: Tech Mining
This issue is developed through 8th Global TechMining Conference (http://www.gtmconference.org/)
Yi Zhang (University of Technology Sydney, Australia), Yi.Zhang@uts.edu.au
Ying Huang (Hunan University, China), email@example.com
Denise Chiavetta (Search Technology Inc., the US), firstname.lastname@example.org
Alan Porter (Georgia Institute of Technology, the US), email@example.com
Tech Mining, a special form of “Big Data” analytics, aims to generate practical intelligence using bibliometric and text-mining software, as well as other analytical & visualization applications, for analyses of Science, Technology & Innovation (ST&I) information resources. Management and policy target uses include Competitive Technical Intelligence (CTI), and university, government, and industry research management (enriching grant applications, scoping projects, avoiding redundant research, and assessing R&D effectiveness). The goal of the special issue is to advance the use of textual information in multiple science, technology, and business development fields by addressing the following key challenges:
- Maximizing potential of traditional and novel text data: Tapping vital traits while minding limitations and matching analytical aims. Data topics of interest include:
- Resolving data hygiene issues generated by multiple sources;
- Ways to address newer data sources (e.g., web scraping, social media cumulations);
- Transitioning from database mining to real-time streaming text-analytics;
- Data structures and lattices;
- Advancing and integrating methods. Topics of interest include:
- Tracking emergence by extracting topical content via advanced natural language processing, term clumping, topic modelling, etc.
- Ways to demonstrate multi-dimensional indicators of R&D & innovation activity
- Means to combine quantitative and qualitative approaches in practical case applications
- Algorithms for identification of emergence (of technologies and inception of notable innovations)
- Discovering the higher-level properties of (complex) social networks
- Translating analyses to useful intelligence: Informative indicators and compelling visualizations.
- Case studies that offer testable projections (specific, explicit) to enable future revisiting to validate methods.
- Visual analytics capabilities, both for analysts and for end-users
While this issue is developed in collaboration with the Global TechMining Conference (GTM) 2018 held in Leiden, 11 September 2018, papers from outside the conference are welcome.
- Submission date change to June 30
- August 30, 2019: 1st Round Reviews Complete
- September 31, 2019: 1st Round Revision Due (for papers for which revisions are requested, i.e., papers that are not rejected)
- October 31, 2019: 2nd Round Review/Revision (Expedited re-reviewing process would be with rapid feedback to authors)
- November 30, 2019: Final Revisions Due (including revisions in response to any second reviews)
- January 2020: Estimated Date of Publication
Please prepare the manuscript according to IEEE-TEM’s guidelines (http://www.ieee-tems.org/guidelines-for-authors) and submit to the journal’s Manuscript Central site (https://mc.manuscriptcentral.com/tem-ieee). Please clearly state in the cover letter that the submission is for this special issue.
IEEE Transactions on Engineering Management is journal of the Technology and Engineering Management Society of IEEE, published quarterly since 1954. It is dedicated to the publication of peer-reviewed original contributions, by researchers and practitioners, regarding the theory and practice of engineering, technology, and innovation management.
Editor in Chief
Tugrul U Daim, PhD PICMET Fellow
Professor and Director
Technology Management Doctoral Program
Department of Engineering and Technology Management
Maseeh College of Engineering and Computer Science
Portland State University, Portland OR