The recently published article "Facilitate Technology Management: From a Technology Life Cycle Perspective" presents a framework for identifying the technology's main evolutionary pathway. What is unique about this framework is that it introduces new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology's development. It also shows how information about a family of patents can be used to build a comprehensive patent citation network. Finally, integrated approaches of main path analysis (MPA)—namely global MPA and global key-route main analysis—are applied for extracting technological trajectories at different technological stages.
An evaluation of reproducible approaches for identifying the emergence of technological novelties in scientific
publications is available in Scientometric's rescently published article, "Evaluating technological emergence using text analytics:
two case technologies and three approaches." The selected approaches are term counting technique, VantagePoint's EScore, and Latent Dirichlet Allocation (LDA). The study finds that each method provides a somewhat distinct perspectives on technological emergence, and offers advantages depending on an analyst's objective. EScore provides a holistic view of emergence by considering several parameters, namely term frequency, size, and origin of the research community compared to the term count based method and LDA.
The VP Institute together with the Beijing Institute of Technology is pleased to announce the 10th Global TechMining Conference (GTM2020) will take place October 16 through 18 of 2020 in Beijing, China.
Tech mining is a text-oriented form of “Big Data” analytics, generating practical intelligence from Science, Technology & Innovation (ST&I) information by applying bibliometric and text-mining software (e.g., Derwent Data Analyzer (DDA), VantagePoint) as well as other analytical & visualization applications. Tech mining supports decision making in ST&I management – e.g., competitive technical intelligence, R&D management, research evaluation, and triple helix analysis. “Big Data” brings both opportunities and challenges. ST&I management must stay vigilant of the changing landscape of data and analytic models, methods, and applications used to track the dynamics of science, technology, the economy, and society. The focus of GTM2020 is to engage cross-disciplinary networks of analysts, software specialists, researchers, and managers to address such challenges and advance text-data-driven solutions for ST&I management. Submission will be accepted through April 15, 2020 at http://www.gtmconference.org/submissions/