Control Systems and Computers

https://doi.org/10.15407/csc.2024.02.010

Control Systems and Computers, 2024, Issue 2 (306), pp. 10-20

UDK  519.816+339.9:339.128

N.K. Tymofijeva, Doctor (Eng.), Senior Research Associate, Head of the Department, International Research and Training Center for Information Technologies and Systems of the NAS and MES of Ukraine, Scopus ID: 7005938396, ORCID: https://orcid.org/0000-0002-0312-1153, Glushkov ave., 40, Kyiv, 03187, Ukraine, tymNad@gmail.com

N.Ye. Pavlenko, Research Associate, International Research and Training Center for Information Technologies and Systems of the NAS and MES of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine, ORCID: https://orcid.org/0009-0005-5660-8669, Pavnata@gmail.com

S.A. Shevchenko, Junior Research Associate, International Research and Training Center for Information Technologies and Systems of the NAS and MES of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine, ORCID: https://orcid.org/0009-0003-2096-3648, shvesta@ukr.net

WAYS OF CLASSIFYNG DIGITAL PLATFORMS

Introduction. Interest in the study of digital platforms (DP) is due to their prevalence and the dependence of this phenomenon on the possibilities of using information technologies. The growing distribution and great potential of the DP is connected not only with the use of new hardware and software, but also with the integration of digital technologies into business processes. The need for a deeper understanding of the differences and similarities of various CPUs prompts researchers to turn to the fundamental mechanism of knowledge organization – classification. From a practical point of view, the classification helps to compare different CPUs and allows users to choose the one that provides the desired results.

Formulation of the problem. The problem of CPUs classification is to identify specific and common characteristics for building clusters using different approaches. When modeling and solving the classification problem, static methods and machine learning methods are used. The most widespread of them are the method of nearest neighbors and the method of support vectors. The theory of combinatorial optimization was used to build the mathematical model.

The approach proposed. To build a mathematical model of the classification problem, the theory of combinatorial optimization was used, which allows to investigate some properties of this problem. The argument of the objective function in it is the division of the -element set into subsets. This combinatorial configuration can be either with or without repetitions, either finite or infinite. When finding the optimal result, a situation of uncertainty arises, which is related to the structure of the argument of the objective function which is a combinatorial configuration.

Conclusion. The classification problem belongs to a broad class of partitioning problems. In it, the characteristics of the clusters are known, the objects that need to be determined, to which class they belong, are analyzed not simultaneously, but by groups or individual elements. Since the result is determined not simultaneously, but by a partial objective function, the classification problem belongs to the dynamic problems of combinatorial optimization. The classification of digital platforms is carried out by heuristic methods, in particular the nearest neighbor method. Both one and a set of common characteristics characteristic of certain CPUs are used as criteria.

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Key words: digital platforms, classification, partitioning of the -element set into subsets, partitioning problems, combinatorial optimization.

1. Klassifikatsija I klaster (1980). Pod redaktsit’j Dj.Ven Ra’jzina / Per. s algl. М.: Мir, 389 p.
2. Faktorny’j, diskriminantny’j i klasterny’j analuz (1989). Dj.-О. Kim, Th.U. Mjuller, U.R. Klekka, М.S. Oldenderfer, R.K. Bleshfikdj; Pod red. I. S. Еnukova / Per. s angl. M.: Finansy i statistsika, 215 p.
3. Jambu, M. (1988). Ierarxitheski’j klaster-analiz i sootvestvija. М: Finansy i statistsika / Per. s angl., 342 p.
4. Tymofiieva, N.K. Teoretyko-chyslovi metody rozviazannia zadach kombinatornoi optymizatsii: avtoref. dys… dokt. tekhn. nauk: 01.05.02. Kyiv: ІK іm. V.M. Gluchkova NАN Ukrainy, 2007. 32 p.
   https://doi.org/10.2118/0102-05-TT
5. Tymofiieva, N.K. (2013). “Pro argument thilovoji funkthiji v zadathax klasterizatsii ta klasifikathii”. Cluster Computing, Second International Conference, Collection of scientific papers, HPC-UA. June 3-5, 2013. Lviv, Ukraine, pp. 221-224.
6. Gawer, A. (2014) “Bridging differing perspectives on technological platforms: Toward an integrative frame-work”. Research policy, 43 (7), pp. 1239-1249. DOI: https://doi.org/10.1016/j.respol.2014.03.006
7. Parker, G., Van Alstyne, M., Choudary, S. (2016). Platform Revolution: How Networked Markets Are Transforming the Economy and How to Make Them Work for You, W.W. Norton & Company, 352 p.
8. Karhu, K., Gustafsson, R., Lyytinen, K. (2018). “Exploiting and Defending Open Digital Platforms with BoundaryResources: Android’s Five Platform Forks”. Information Systems Research, no 29 (2), рр. 479-497. https://doi.org/10.1287/isre.2018.0786
9. Arnold, L., Johnk, J., Vogt, F., Urbach, N. (2021). “A taxonomy of IoT platforms’ architectural features”. Internationale Konferenz Wirtschaftsinformatik 2021, Essen, Germany, pp. 404-421.
   https://doi.org/10.1007/978-3-030-86800-0_28
10. Kenney, M., Zisman, J. (2016). “The Rise of the Platform Economy. Issues in Science and Technology, 32 (3), pp. 61-69.
11. Kuprevich, T. (2018). “Digital platforms in the world economy: current trends and directions of development. University Economic Bulletin, no 37/1, pp. 311-318.
12. Sichkarenko, K. (2018). “Digital platforms: classification approacing and the role of economic development defining”. Black Sea Economic Studies, no 35, pp. 28-32.
13. Semenog, A. (2019). “Conceptual principles of development of digital platforms in the conditions of the formation of a digital economy”. Scientific notes of the National University “Ostroh Academy”. “Economy” series, No. 14 (42), pp. 21-26. DOI:
    https://doi.org/10.25264/2311-5149-2019-14(42)-21-26
14. Kokhan, V. (2021). “Digital platform as a digital economy tool”. Law and innovations, 1 (33), C. 29-34 (In Ukrainian). https://doi.org/10.37772/2518-1718-2021-1(33)-4
15. Blaschke, M., Haki, K., Aier, S., Winter, R. (2019). “Taxonomy of Digital Platforms: A Platform Architecture Perspective”. 14 th International Conference on Wirtschaftsinformatik, February 24-27, Siegen, Germany, pp. 572-586.
16. Staub N., Haki K., Ai S., Winterer R. Taxonomy of Digital Platforms: A Business Model Perspective. 54th Hawaii International Conference on System Sciences, 2021.
    https://doi.org/10.24251/HICSS.2021.744
17. Arnold, L., Johnk, J., Vogt, F., Urbach, N. (2021). “A Taxonomy of Industrial IoT Platforms’ Architectural Features”. Internationale Konferenz Wirtschaftsinformatik 2021, March, Essen, Germany.
    https://doi.org/10.1007/978-3-030-86800-0_28

Received 15.02.2024