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Development of big data analysis approaches for research of global technological trends

Priority areas of development: economics, management
2019
Department: International Research and Educational Foresight Centre

Goal of research

Development of methodology for research of global technological trends based on the advancement of big data analysis approaches, particularly text mining techniques, and aimed at actualization of the results of global technological trends monitoring obtained in 2018 for priority science and technology (S&T) areas as well as establishment of new formats of trendletters and preparation of analytical report on their basis.

Methodology

This research project employs an advanced methodology for global technological trends analysis. In particular, this study uses an intellectual system of big data analysis (iFORA), which includes natural language text processing (segmentation, tokenization, lemmatization, stemming, morphological and syntactic analysis); relevant scientific and technical terms extraction from the sentences of the texts; recognition of the names of organizations, people and geographical locations in the texts; machine learning testing, especially teaching the distribution semantic model word2vec / parast2vec / doc2vec; calculation of text embeddings of scientific and technical terms, documents, scientists and organizations; statistical analysis. This methodological approach is innovative and has no analogues in domestic and foreign studies.

Empirical base of research

The development of new methodological approaches made it possible to further develop the empirical base of the study, which includes, not only newsletters (trendletters) created over the years of the existence of this monitoring project and reports published on their basis, but more than 350 million documents (including reports of scientific conferences, grants, research reports, international scientific publications and patents, educational programs, documents between arodnyh organizations and consulting companies, market analysis and professional media, the legal framework, information on vacancies) that are analyzed by the intelligent big data analysis system.

Results of research

In theory,the development of theoretical approaches to global technological trends analysis with the approbation of text embeddings building for the scientific and technical terms, documents, scientists and organizations in a single vector space was achieved. In methodology, the construction of vector representations of scientific and technical documents for the implementation of text embeddings was considerably improved. In empirical knowledge database, a deeper understanding of global technological trends diversity and complexity (in terms of effects, drivers, barriers, structural features) for the priority S&T areas was obtained as a result of more accurate analysis, conducted by both machine learning algorithms (iFORA) and experts.

Level of implementation, recommendations on implementation or outcomes of the implementation of the results

The combination of improved quantitative and qualitative methods contributed to the increasing efficiency of the identification process of the global technology trends, as well as the accuracy of their assessment and description.

The results of this study can be used to develop scientific, technological and socio-economic forecasts, strategic and budget forecasts of the Russian Federation; to create state federal targeted, sectoral and regional programs and roadmaps; to implement research and development programs; to make corporate strategies for companies; to design training programs, etc.

 

Publications:


Milshina Y., Vishnevskiy K. Roadmapping in fast changing environments – the case of the Russian media industry // Journal of Engineering and Technology Management - JET-M. 2019. Vol. 52. P. 32-47. doi
Proskuryakova L. N., Ermolenko G. The future of Russia’s renewable energy sector: trends, scenarios and policies // Renewable Energy. 2019. Vol. 143. P. 1670-1686. doi
Milshina Y., Pavlova D. Future prospects for the management of water resources in Russia using indigenous technical knowledge, in: Water Conservation and Wastewater Treatment in Brics Nations.: Elsevier, 2020. С. 297-319. 
Maria T., Vishnevskiy K., Zarubin A. Technology assessment of IoT wireless network technologies for the telecommunication sector / ГУ ВШЭ. Series SCIENCE, TECHNOLOGY, INNOVATION "SCIENCE, TECHNOLOGY, INNOVATION". 2019. No. WP BRP 94/STI/2019.