A Systems Approach to the Architecture Design of Analytical Digital Platforms

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Abstract

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.

About the authors

A. A. Shamin

Nizhny Novgorod State Engineering and Economic University

Email: al.shamin@mail.ru
ORCID iD: 0000-0003-4138-6256

M. O. Kolbanev

Saint Petersburg State Electrotechnical University "LETI"

Email: mokolbanev@mail.ru
ORCID iD: 0000-0003-4825-6972

A. D. Cheremuhin

Nizhny Novgorod State Engineering and Economic University

Email: ngieu.cheremuhin@yandex.ru
ORCID iD: 0000-0003-4076-5916

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