Skip to main content
SpringerLink
Log in

Digital Environment: Information Analytical Postprocessing Using the Scientometric and Data Analysis Methods

  • Published:
Scientific and Technical Information Processing Aims and scope

Abstract

This article studies the macrostructure and dynamics of the growth of the global digital environment. It presents the possibilities and application areas of scientometrics and data analysis methods for the production of information and analytical products and services. It briefly analyzes the base of potential data sources for the problems of analytical postprocessing and prospective approaches to more in-depth information processing and new knowledge retrieval. Considering the rapid development of the online infrastructure of science and digital transformation of the information space, the main conceptual theses of implementation of technologies and information analytical postprocessing systems are formulated. Theoretical and applied aspects of analytical postprocessing within the structure of Big Data technologies are discussed. Some determinant factors of implementation of domestic Digital Economy of The Russian Federation program are presented. The goal of this work was to demonstrate the potential and system role in the formation of the new information environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

REFERENCES

  1. The growth of information—realities of the digital universe, Tekhnol. Sredstva Svyazi, 2013, no. 1. http://www.tssonline.ru/articles2/fix-corp/rost-obema-informatsii-. Accessed May 27, 2018.

  2. Syuntyurenko, O.V., The digital enviroment: The trends and risks of development, sci. Tech. Inf. Process., 2015, vol. 42, no. 1, pp. 24–29.

    Article  Google Scholar 

  3. The development of mobile Internet as predicted by Cisco. http://1234g.ru/novosti/rasvitie-mobilnogo-interneta. Accessed May 27, 2018.

  4. Kusaikin, D., Global network traffic: the present and the future, 2017. https://Nag.ru/articles/article/31463/mirovoy-setevoy-trafik-nast. Accessed May 27, 2018.

  5. Big information explosion. The size of Internet content is rapidly changing the infosphere of the Earth, Russ. Rep., 2017, no. 2, pp. 52–53.

  6. Brynjolfsson, E. and McAfee, A., The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, New York: Norton & Company, 2016.

    Google Scholar 

  7. Fursov, A.I., Vodorazdel. Budushchee, kotoroe uzhe nastupilo (Watershed. A Future That Is Already Here), Moscow: Kn. mir, 2018.

  8. Borisova, L.F. and Syuntyurenko, O.V., VINITI RAN Abstract Database: Prospects of information postprocessing using methods of data analysis, Sci. Tech. Inf. Process., 2007, vol. 34, no. 6, pp. 278–283.

    Article  Google Scholar 

  9. Syuntyurenko, O.V., Making information and analytical products and services using the methods of scientometrics and data analysis, Materialy Mezhdunarodnoi konferentsii k 65-letiyu VINITI RAN “Informatsiya v sovremennom mire” (Proc. Int. Conf. on the 65th Anniversary of the VINITI RAS “Information in the Modern World”), Moscow, 2017, pp. 317–321.

  10. Tukey, J.W., Exploratory Data Analysis, Addison-Wesley Publishing Company, 1977.

    MATH  Google Scholar 

  11. Mosteller, F. and Tukey, J.W., Data Analysis and Regression: A Second Course in Statistics, Pearson, 1977.

    Google Scholar 

  12. Syuntyurenko, O.V., Theoretical and applied aspects of automating multivariate analysis procedures, Autom. Doc. Math. Linguist., 2018, vol. 52, no. 6, pp. 275–281.

    Article  Google Scholar 

  13. Kalachikhin, P.A., A methodology for the scientometric expert evaluation of research results, Autom. Doc. Math. Linguist., 2017, vol. 51, no. 2, pp. 53–61.

    Article  Google Scholar 

  14. Kalachikhin, P.A., The principles of the design of the state scientometric system, Autom. Doc. Math. Linguist., 2016, vol. 50, no. 4, pp. 161–172.

    Article  Google Scholar 

  15. Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffmann, A., and Giovannini, E., Handbook on constructing composite indicators, in OECD Statistics Working Papers, 2005, vol. 3.

  16. Kogalovskii, M.R. and Parinov, S.I., A new data source for scientometric studies, Trudy 15-i Vserossiiskoi nauchnoi konferentsii “Elektronnye biblioteki: perspektivnye metody i tekhnologii, elektronnye kollektsii”—RCDL-2013 (Yaroslavl’, Rossiya, 14–17 oktyabrya 2013 g.) (Proc. 15th All-Russ. Sci. Conf. Electronic Libraries: Advanced Methods and Technologies, Digital Collections, RCDL-2013 (Yaroslavl, Russia, October 14–17, 2013)), Yaroslavl, 2013, pp. 107–117.

  17. Antoshkova, O.A., Beloozerov, V.N., Dmitrieva, E.Yu., et al., Building the ontology of information resources in the form of a network of bibliographic classifications, Perspektivnye napravleniya issledovanii i kriticheskie tekhnologii v klassifikatsionnykh sistemakh: Nauchno-prakticheskaya konferentsiya s inostrannym uchastiem (25–27 okt. 2017 g.) (Perspective Research Directions and Critical Technologies in Classification Systems: Scientific-Practical Conference with Foreign Participation (October 25–27, 2017)), Moscow, 2017, pp. 20–25.

  18. Kondratiev, N.D., Bol’shie tsikly kon"yunktury i teoriya predvideniya (Great Surges of Business Climate and Anticipation Theory), Moscow: Ekonomika, 2002.

  19. Perez, C., Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages, London: Elgar, 2002.

    Book  Google Scholar 

  20. Glaz’ev, S. and Mikerin, G., Dlinnye volny NTP i sotsial’no-ekonomicheskoe razvitie (Long Waves of the Scientific and Technical Progress and Socio-Economic Development), Moscow: Nauka, 1989, pp. 5–9.

  21. Ivanov, V. and Malinetskii, G., Digital economy: Myths, reality, and prospects, in Tsifrovaya tsivilizatsiya. Rossiya i “elektronnyi mir” XXI veka (Digital Civilization. Russia and the “Electronic World” of the 21st Century), Moscow: Izborskii klub, Kn. mir, 2018.

  22. Mesropyan, V.R. and Ovsyannikov, M.V., Prospects for the application of scientometric methods for forecasting, Sci. Tech. Inf. Process., 2014, vol. 41, no. 1, pp. 38–46.

    Article  Google Scholar 

  23. Avdulov, A.N. and Kul’kin, A.M., Finansirovanie nauki v razvitykh stranakh mira (Science Funding in Developed Countries), Moscow: Inst. Nauchn. Inf. Obshchestv. Nauk Ross. Akad. Sci., 2007.

  24. Syuntyurenko, O.V. and Gilyarevskii, R.S., Using the methods of scientometrics and comparative data analysis for managing research in thematic areas, Nauchno-Tekh. Inf., Ser. 2, 2016, no. 12, pp. 1–12.

  25. Kalachikhin, P.A., Scientometric instruments of research funding, Sci. Tech. Inf. Process., 2018, vol. 45, no. 1, pp. 28–34.

    Article  Google Scholar 

  26. Syuntyurenko, O.V., Funding for basic research: A conceptual image of a decision support system using scientometrics and data analysis methods, Inf. Primen., 2018, vol. 12, no. 1, pp. 118–127.

    Google Scholar 

  27. Drozdova, K.A., Machine translation: History, classification, and methods, in Filologicheskie nauki v Rossii i za rubezhom: Materialy III Mezhdunar. nauch. konf. (Philological Sciences in Russia and Abroad: Proc. III Int. Sci. Conf.), St. Petersburg, 2015, pp. 139–141. https://moluch.ru/conf/phil/archive/138/8497. Accessed December 28, 2018.

  28. Kolganov, D.S. and Danilov, E.A., Overview of analytical, statistical and neural machine translation technology, Int. Stud. Sci. Bull., 2018, no. 3-2. http://eduherald.ru/ru/article/view?id=18262. Accessed December 28, 2018.

  29. Antopolskii, A.B., On the feasibility of the Russian National Webometric Index, Sci. Tech. Inf. Process., 2014, vol. 41, no. 1, pp. 33–37.

    Article  Google Scholar 

  30. Bulycheva, O.S. and Syuntyurenko, O.V., Conceptual provisions and prerequisites for creating a webometric system of digital space of libraries, Sb. Prez. Bibl., Ser. Elektron. Bibl., 2018, vol. 8, pp. 19–31.

    Google Scholar 

  31. Syuntyurenko, O.V., Determinants of the ineffective use of information resources in scientific and technological activities, Sci. Tech. Inf. Process., 2017, vol. 44, no. 3, pp. 159–169.

    Article  Google Scholar 

  32. King, W.D. and Bryant, C.E., The Evaluation of Information Services and Products, Washington: Information Resources Press, 1971.

    Google Scholar 

Download references

Funding

This work was funded by the Russian Scientific Foundation, grant no. 17-07-153.

Author information

Authors and Affiliations

  1. All-Russian Institute for Scientific and Technical Information, Russian Academy of Sciences, 125190, Moscow, Russia

    O. V. Syuntyurenko

Authors
  1. O. V. Syuntyurenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to O. V. Syuntyurenko.

Ethics declarations

The authors declare that they have no conflict of interest.

Additional information

Translated by A. Dunaeva

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Syuntyurenko, O.V. Digital Environment: Information Analytical Postprocessing Using the Scientometric and Data Analysis Methods. Sci. Tech. Inf. Proc. 46, 59–66 (2019). https://doi.org/10.3103/S0147688219020047

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0147688219020047

Keywords:

Search

Navigation