A Systems Approach to the Architecture Design of Analytical Digital Platforms

In the context of the rapid development of the digital economy, the architectures of digital platforms have become a key subject of scientific and applied analysis. Most existing taxonomies and classifications of digital platforms focus on goals, functions, or business models, while architectural aspects often remain insufficiently structured. This issue is particularly relevant for analytical digital platforms, which combine the functionality of traditional digital systems with machine learning methods, thus requiring a comprehensive systems-based approach to their description and design.

The aim of the study is to systematize and analyze the architectural components of digital platforms from the standpoint of various approaches to system analysis, as well as to design a prototype of the functional architecture of a digital analytical platform using the example of the agricultural sector. The research employs methods of systems analysis, taxonomic modeling, comparative typology, and architectural design synthesis using functional, structural, object-oriented, cybernetic, network-based, evolutionary, and ontological approaches.

The result is a generalized model of the architecture of an analytical digital platform, identifying its subsystems, elements, relationships, boundaries, environment, and identifiers according to each of the seven systems analysis approaches. As a practical example, the architecture of a prototype platform for analyzing the profitability of agricultural organizations is developed, implementing a pipeline for data processing, analysis, forecasting, and visualization.

The novelty of the study lies in the comprehensive application of all major systems analysis approaches to the description of analytical platform architectures and in the formalization of an architecture that integrates data levels, models, scenarios, and ontological entity descriptions.

The practical significance of the work is the potential use of the proposed architectural model in the design
of digital decision-support platforms in industries requiring advanced analytics.

1. Arnold L., Jöhnk J., Vogt F., Urbach N. A Taxonomy of Industrial IoT Platforms’ Architectural Features. Proceedings of the 16th International Conference on Wirtschaftsinformatik “Innovation Through Information Systems. Volume III: A Collection of Latest Research on Management Issues”, WI 2021, 9–11 March 2021. Lecture Notes in Information Systems and Organisation, vol.48. Cham: Springer; 2021. p.404–421. DOI:10.1007/978-3-030-86800-0_28

2. Diniz E.H., Siqueira E.S., van Heck E. Taxonomy of digital community currency platforms. Information Technology for Development. 2019;25(1):69–91. DOI:10.1080/02681102.2018.1485005

3. da Silva Neto V.J., Chiarini T. The Platformization of Science: Towards a Scientific Digital Platform Taxonomy. Minerva. 2023;61:1–29. DOI:10.1007/s11024-022-09477-6. EDN:WXTASP

4. Blaschke M., Haki K., Aier S., Winter R. Taxonomy of Digital Platforms: a Platform Architecture Perspective. Proceedings of the 14th International Conference on Wirtschaftsinformatik, 24–27 February 2019, Siegen, Germany. p.572–586.

5. Kutliev G., Babaev I. Managing the Digital Economy with Artificial Intelligence: A New Level of Efficiency. Simvol nauki: mezhdunarodnyi nauchnyi zhurnal. 2024;1(10-2):127–128. (in Russ.) EDN:NVYVIN

6. Glinskiy V.V., Serga L.K. On measurement of the results of the activities of digital economy at the regional level. Vestnik NSUEM. 2022;4:219–233. (in Russ.) DOI:10.34020/2073-6495-2022-4-219-233. EDN AMMOOW

7. Arkhipova Z.V. The concept of information system for monitoring digital economy development level. Baikal Research Journal. 2018;9(3):8. (in Russ.) DOI:10.17150/2411-6262.2018.9(3).8. EDN:TUXJWW

8. Ivinskaya E.Yu., Shevko N.R., Khisamutdinova E.N. The Assessment of the Level of development of the Information Economy Based on the Status of Digital Infrastructure Objects. Gorizonty ekonomiki. 2020;6(59):26–31. (in Russ.) EDN:CHVVSG

9. Kryshtanosay V.B. Threats and risks of digital economy at the sectoral level. Trudy BGTU. Seriia 5, Ekonomika i upravlenie. 2022;1(256):28–52. (in Russ.) DOI:10.52065/2520-6877-2022-256-1-28-52. EDN:ZOERMC

10. Yakimova T.B. Digital economy and its impact on the level and quality of life population. Russian Economic Bulletin. 2022;5(1):245–250. (in Russ.) EDN:WYCFMH

11. Viola N., Corpino S., Fioriti M., Stesina F. Functional Analysis in Systems Engineering: Methodology and Applications. In: Cogan B. (ed.) Systems Engineering – Practice and Theory. InTech; 2012. p.71–96. DOI:10.5772/34556

12. Cutts G. Structured systems analysis and design methodology. 1988. URL: https://api.semanticscholar.org/CorpusID:108576776 [Accessed 11.09.20205]

13. Dennis A., Wixom B., Tegarden D. Systems Analysis and Design. An Object-Oriented Approach with UML. Wiley; 2015.

14. Kharchenko V., Dotsenko S., Ponochovnyi Yu., Illiashenko O. Cybernetic approach to developing resilient systems: Concept, models and application. Information & Security. 2020;47(1):77–90. DOI:10.11610/isij.4705. EDN:SFHPWS

15. Anderson B.D.O., Vongpanitlerd S. Network Analysis and Synthesis: A Modern Systems Theory Approach. Courier Corporation; 2013.

16. Majone G. Applied Systems Analysis: A Genetic Approach. 1980.

17. Rosemann M., Green P., Indulska M. A Reference Methodology for Conducting Ontological Analyses. Proceedings of the 23rd International Conference on Conceptual Modeling, 8–12 November 2004, Shanghai, China. Berlin, Heidelberg: Springer; 2004. p.110–121. DOI:10.1007/978-3-540-30464-7_10

18. Derave T., Sales T.P., Gailly F., Poels G. Understanding Digital Marketplace Business Models: An Ontology Approach. Proceedings of workshops co-organized with the 14th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modelling, Po-EM 2021, 24 November 2021, Riga, Latvia, vol.3031. CEUR; 2021. p.15–26.

19. Armstrong E.M., Bourassa M.A., Cram T.A., DeBellis M., Elya J., Greguska III F.R., et al. An Integrated Data Analytics Platform. Frontiers in Marine Science. 2019;6:354. DOI:10.3389/fmars.2019.00354

20. Cheremukhin A.D., Shamin A.A., Kolbanev M.O., Tsekhanovskii V.V. The Effectiveness of the SVM Method in the Task of Determining Profitable Organizations. Proceedings of Saint Petersburg Electrotechnical University. 2023;16(4):30–45. (in Russ.) DOI:10.32603/2071-8985-2023-16-4-30-45. EDN BFFLWR

21. Cheremukhin A.D., Shamin A.A., Kolbanev M.O., Tsekhanovskii V.V. Using SVM to Study Dynamics of Factors Affecting the Profitability of Agricultural Organizations. Proceedings of Saint Petersburg Electrotechnical University. 2021;2:58–68. (in Russ.) EDN:YKOARK