I.P. Kameneva, V.O. Artemchuk

Èlektron. model. 2022, 44(3):50-64


The problem of informativeness and definition of informative structures is considered in the framework of the modern concept of Big Data Analytics, which integrates a series of approaches, methods, and tools for analyzing structured and unstructured data of large volumes. The main trends and prospects of Big Data Analytics for identifying knowledge and patterns important for decision making are highlighted. The analysis of criteria of informativeness of a set of parameters and the means directed on reduction of dimensionality of space of initial signs is carried out. Possibilities of methods of revealing informative parameters for decision-making support within a wide range of ecological and energy security tasks are determined. The general structure and algorithm of construction of a knowledge base for decision-making in conditions of uncertainty and risk are offered.


informativeness, informative parameters, Big Data Analytics, latent knowledge, decision making.


  1. Mayer-Schenberger, V. and Kukier, K. (2014), Bolshiye dannyye. Revolyutsiya, kotoraya izmenit to, kak my zhivem, rabotayem i myslim [Big data. A revolution that will change the way we live, work and think], Mann, Ivanov i Ferber, Moscow, Russia.
  2. Veres, O.M. (2018), Classification of  methods  for  the  analysis  of  big data, available at:
  3. Duke, V. and Samoylenko, A. (2001), Data Mining, Piter, St. Petersburg, Russia.
  4. Putrenko, V.V. (2015), “System bases of intellectual analysis of geospatial data”, Systemni doslidzhennya ta informatsiyni tekhnolohiyi, Vol. 3, pp. 20-33.
  5. Yatsyshyn, A.V., Kutsan, Y.G., Artemchuk, V.O. et al. (2019), “Means of intellectual analysis and visualization of geospatial atmospheric air monitoring data”, Elektronne modelyuvannya, Vol. 41, no. 5, pp. 85-102.
  6. Aivazyan, S.A., Bushtaber, V.M., Enyukov, I.S. and Meshalkin, L.D. (1989), Prikladnaya statistika. Klassifikatsiya i snizheniye razmernostey [Applied statistics. Classification and dimensionality reduction], Finansy i statistika, Moscow, USSR.
  7. Terekhina, A.Y. (1988), Predstavleniye struktury znaniy metodami mnogomernogo shkalirovaniya [Representation of knowledge structure by multidimensional scaling methods], VINITI, Moscow, USSR.
  8. Zagoruiko, N.G. (1999), Prikladnyye metody analiza dannykh i znaniy [Applied methods of data and knowledge analysis], IM SO RAN, Novosibirsk, Russia.
  9. Kulbak, S. (1967), Teoriya informatsii i statistika [Theory of information and statistics], Nauka, Moscow, USSR.
  10. Zagoruiko, N., Borisova, I. and Kutnenko, O. (2007), “Criteria of informativeness and suitability of a subset of features”, International Conference "Knowledge - Dialogue - Solutions", available at:­sova_Zagoruiko_Kutnenko.pdf
  11. Van Waarde, J., Eising, J., Trentelman, H. and Çamlibel, M. (2019), “Data Informa­tivity: A New Perspective on Data-Driven Analysis and Control”, Published 1 August 2019 Computer Science, Mathematics IEEE Transactions on Automatic Control, available at: rspective-on-Analysis-Waarde-Eising/6f6958870664a58e98d93af94d80aa9d3409030c.
  12. Fazilov, S, Mamatov, N. and Samijonov, A. (2019), “Selection of Significant Features of Objects in the Classification Data Processing”, International Journal of Recent Technology and Engineering (IJRTE), Vol. 8, Issue-2S11, ISSN: 2277-3878.
  13. Ilnitsky, A.I. and Burba, O.I. (2019), “Statistical criteria for assessing the informativeness of the signs of radio sources of telecommunications networks and systems in their recognition”, Kiberbezpeka: osvita, nauka, tekhnika, Vol. 1, no. 5, pp. 83-94.
  14. Kameneva, I.P. (2005), “Spatial-semantic models of knowledge representation in geoecological research”, Heoinformatyka, Vol. 4, pp. 64-69.
  15. Kameneva, I.P. (2013), “The modeling of semantic space of expert knowledge from different sources”, Modelyuvannya ta informatsiyni tekhnolohiyi, Vol. 70, pp. 3-10.
  16. Kameneva, I.P., Artemchuk ,V.O. and Yatsyshyn, A.V. (2019), “Probabilistic modeling of expert knowledge using psychosemantics methods (using environmental data as an example)”, Elektronne modelyuvannya, Vol. 41, no. 5, pp. 85-102.
  17. Anderson, J. (2002), Kognitivnaya psikhologiya [Cognitive Psychology], Piter, St. Petersburg, Russia.
  18. Kini, R. (1981), “Theory of decision making”, Research of operations: in 2 volumes, Vol. 1, 481-512.
  19. Kameneva, I.P., Artemchuk, V.O., Yatsyshyn, A.V. and Bugaev, A.F. (2017), “Cognitive decision-making strategies based on probabilistic estimates and risk maps”, Modelyuvannya ta informatsiyni tekhnolohiyi, Vol. 80, pp. 20-27.

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