Big-Data-Augmented Approach to Emerging Technologies Identification: Case of Agriculture and Food Sector

Abstract

The paper discloses a new approach to emerging technologies identification, which strongly relies on capacity of big data analysis, namely text mining augmented by syntactic analysis techniques. It discusses the wide context of the task of identifying emerging technologies in a systemic and timely manner, including its place in the methodology of foresight and future-oriented technology analysis, its use in horizon scanning exercises, as well as its relation to the field of technology landscape mapping and tech mining. The concepts of technology, emerging technology, disruptive technology and other related terms are assessed from the semantic point of view. Existing approaches to technology identification and technology landscape mapping (in wide sense, including entity linking and ontology-building for the purposes of effective STI policy) are discussed, and shortcomings of currently available studies on emerging technologies in agriculture and food sector (A&F) are analyzed. The opportunities of the new big-data-augmented methodology are shown in comparison to existing results, both globally and in Russia. As one of the practical results of the study, the integrated ontology of currently emerging technologies in A&F sector is introduced. The directions and possible criteria of further enhancement and refinement of proposed methodology are contemplated, with special attention to use of bigger volumes of data, machine learning and ontology-mining / entity linking techniques for the maximum possible automation of the analytical work in the discussed field. The practical implication of the new approach in terms of its effectiveness and efficiency for evidence-based STI policy and corporate strategic planning are shortly summed up as well.

Twenty-First Century Skills in Finance: Prospects for a Profound Job Transformation

“…Over the course of this study we used various quantitative and qualitative techniques including text mining, case studies, and expert interviews. To analyze the current state of the subject area and identify major skill and competency demand trends, the semantic analysis of academic papers and publications in industry-specific media on the future of the labor market was conducted, along with vacancies published on Russian and international websites such as job vacancy aggregators using the iFORA Intelligent Data Analytics System designed by the HSE Institute for Statistical Studies and Economics of Knowledge [Bakhtin et al, 2017;Gokhberg et al, 2017]. Additionally, a collection of more than a hundred case studies was assembled from open sources, reflecting the practices of applying technological solutions in selected areas by banking and other organizations.…”