Effective data model selection for infological entities in multimodel database systems

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Abstract

The article addresses the problem of selecting effective data models for infological entities in the context of designing multimodel databases. The focus is placed on the need for a systematic approach when modeling heterogeneous entities whose structure and behavior require different forms of representation. The study analyzes the characteristics of three widely used models – relational, graph, and multidimensional – in terms of their applicability to various types of infological entities. Key criteria influencing model selection are described, including data structure, interconnectivity, query patterns, mutability, scalability, and consistency requirements. A decision-making algorithm is proposed, based on analyzing entity characteristics and the system’s non-functional requirements. Particular attention is given to the advantages and challenges of multimodel solutions, as well as principles of coordinating different models within a unified architectural framework. The work aims to provide a methodological foundation for rational model selection and for enhancing the adaptability and sustainability of information systems.

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About the authors

Nikita S. Mishin

Bauman Moscow State Technical University

Author for correspondence.
Email: stancuem@yandex.ru
ORCID iD: 0009-0008-3076-8076
SPIN-code: 2572-3667

postgraduate student, Department of Information Processing and Control Systems

Russian Federation, Moscow

Gennady I. Afanasyev

Bauman Moscow State Technical University

Email: gaipcs@bmstu.ru

Cand. Sci. (Eng.), Associate Professor; associate professor

Russian Federation, Moscow

Rustam Z. Khayrullin

Bauman Moscow State Technical University; Moscow State University of Civil Engineering (National Research University)

Email: zrkzrk@list.ru
ORCID iD: 0000-0002-0596-4955
SPIN-code: 6631-0932

Dr. Sci. (Eng.), Senior Scientific Worker; Professor

Russian Federation, Moscow; Moscow

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Supplementary files

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2. Fig. 1. Experimental methodology

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3. Fig. 2. Clustering of data in three-dimensional space

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4. Fig. 3. Polynomial functions for each data model

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