Increasing the operability of environmental monitoring by data mining of info-communication processes

V.P. Evstigneev,  D.Yu. Voronin,  A.V. Skatkov,  Yu.V.Tacyi

 Sevastopol State University, RF, Sevastopol, Universitetskaya St., 33


DOI: 10.33075/2220-5861-2019-4-54-59

UDC 621.391


     In present study features of intellectual analysis of processes of informational service of environmental monitoring systems in order to increase the efficiency of decisions are considered. A complex approach is focused on improving the efficiency of environmental monitoring based on the intellectual analysis of info-communication processes. The potential of using modern tools for clustering info-communication network objects in accordance with the characteristic features of the dynamics of their functioning in solving environmental monitoring problems is shown.

     The authors revealed a typical “profiles” of the load by a non-hierarchical method of cluster analysis k-medoids, which is a robust analogue of the more common method of k-means. Registration data of daily load on 133 switch nodes of the info-communication network of Internet provider for the period 01.06.2018 to 01.04.2019 was used in this study. The data consists of chronological series of the total duration of Internet traffic through the switch for each day. The objective classification resulted in four typical profiles (clusters) of intra-week load. The following analysis strategy was chosen: 1) application of the Principal Components Analysis (PCA) to establish the main modes of load variability in the switch-node networks; 2) spectral analysis of PCA-scores.

     The final section of the study has discussion of examples of the suggested approach application, in particular, for the tasks of building an environmental monitoring system. The data of the info-communication network has characteristic timescale corresponded to a week. In the real environmental monitoring system, natural processes are objects of monitoring as a rule and many of them have similar characteristic timescale of its evolution (for example, synoptic processes develop within 3-7 days).

Keywords: environmental monitoring, data mining, info-communication system, information services, classification, R.

To quote, follow the DOI link and use the Actions-Cite option or copy:
[IEEE] V. P. Evstigneev, D. Y. Voronin, A. V. Skatkov, and Y. V. Tacyi, “INCREASING THE OPERABILITY OF ENVIRONMENTAL MONITORING BY DATA MINING OF INFO-COMMUNICATION PROCESSES,” Monitoring systems of environment, vol. 4, pp. 54–59, Dec. 2019.

Full text in PDF(RUS)


  1. Zhukova N.A. General and particular problems of multi-level synthesis of models of monitoring objects // Scientific and technical information. Series 2: Information processes and systems. 2019. № 11. P. 16–22.
  2. Ponomarev, D. Yu., Theory of teletraffic: textbook Krasnoyarsk: Of SibSAU named after M. F. Reshetnev, 2017. 160 p.
  3. Proactive and reactive risk management of cloud IT services / D.Yu. Voronin, A.V. Skatkov, V.I. Shevchenko, et al. // Information and control systems. 2017. № 3 (88). P. 25–33.
  4. The concept of proactive management of complex objects: theoretical and technological foundations / M. Yu. Okhtilev, N. G. Mustafin, V. E. Miller et al // News of universities. Instrument making. 2014. I. 57. № 11. P. 7–14.
  5. Macal C., North M. Tutorial on agent-based modeling and simulation // Journal of Simulation. 2010. I. 4. P. 151–162.
  6. Jolliffe I.T. Principal Component Analysis, 2nd edn. Springer: New York, 2002. 518 p.
  7. Analysis of multidimensional data. Elected head / translated from English. S. V. Kucherevskogo / under the editorship of O. E. Rodionova. Chernogolovka: Publishing house of IPHF RAS, 2005. 160 p.
  8. North G.R., Bell T.L., Cahalan R.F., Moeng F.J. Sampling errors in the estimation of empirical orthogonal functions // Monthly Weather Review. 1982. Vol. 110. P. 699–706.
  9. Core Team R. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2012. ISBN 3-900051-07-0, URL: (дата обращения: 13.08.2019).
  10. Jenkins G., Watts D. Spectral analysis and its applications. Vol. 1. М.: MIR, 1971. 316 p.
  11. Sergienko A. B. Digital signal processing: a textbook for universities. SPb: Piter, 2002. 608 p.
  12. Schubert E., Rousseeuw P.J. Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms. In: Amato G., Gennaro C., Oria V., Radovanović M. (eds) Similarity Search and Applications. SISAP 2019. Lecture Notes in Computer Science. Vol. 11807. Springer, Cham.
  13. Tibshirani R., Walther G., Hastie T. Estimating the number of clusters in a data set via the gap statistic // J. R. Statist. Soc.B. 2001. Vol. 63. P. 411–423.