Lobachevskii Journal of Mathematics

, Volume 40, Issue 11, pp 1837–1847 | Cite as

Assessments of the Economic Sectors Needs in Digital Technologies

  • A. N. RaikovEmail author
  • A. N. ErmakovEmail author
  • A. A. MerkulovEmail author


An assessment of the economic sectors needs for the solutions based on end-to-end technologies (E2ET) using the Technology readiness level (TRL) and Manufacture readiness level (MRL) metrics has been carried out. The created “E2ET-needs map” matrix shows the needs of nine sectors of the economy in nine blocks of E2ET. It reflects the priorities of the required state support for the development of E2ET in the different economy sectors. It was considered that each block of E2ET includes dozens of sub-technologies. A 3-level tree of about 30 needs assessment criteria were built. Given the presence of many non-quantitative factors that characterize the E2ET-needs the author’s convergent approach including the cognitive modelling and network expertise (e-Expertise) technology was applied. The approach is based on the combination of methods of inverse problems solving on topological spaces, controllable thermodynamics, and category theory. For synthesis and verify cognitive models and the reliability of the experts’ work the Big Data analysis was exploited. Domestic databases of media publications were used as verification arrays. The approach ensures the creation of the necessary conditions for achieving the strategic goals of the development of the digital economy in Russia using the E2ET. The rules of regulation of the annual monitoring of the E2ET needs under consideration were proposed.

Keywords and phrases

Big data cognitive modelling convergent approach end-to-end technologies needs map matrix semantics technology readiness levels 


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This paper contains the results of the project “Verification of cognitive models using Big Data analysis methods to support collective decision-making, including in the System of distributed situational centers and in emergency situations” carried out within the framework of the implementation of the Program of the Center for Competence of the National Technology Initiative Big Data Storage and Analysis Center, supported by the Ministry of Science and Higher Education of the Russian Federation under the Lomonosov Moscow State University treaty with the National Technology Initiative Projects Support Fund no. 13/1251/2018 dated December 11, 2018. This work was also funded by the Russian Foundation for Basic Research, grant 18-29-03086.


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Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.Moscow State UniversityMoscowRussia
  2. 2.Trapeznikov Institute of Control Sciences of Russian Academy of SciencesMoscowRussia
  3. 3.3i TechnologiesMoscowRussia
  4. 4.OOO Agentstvo Novykh StrategiiMoscowRussia

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