Model for choosing a database architecture for storing and processing heterogeneous data using fuzzy logic and neural networks

The problem of choosing a database architecture for storing and processing heterogeneous and dynamically changing data is considered. Modern information systems generate large amounts of structured, semi-structured and unstructured data from multiple sources, which makes the task of choosing an architecture multi-criteria and associated with high uncertainty. An intelligent decision support model based on a hybrid approach combining the methods of fuzzy logic and neural network analysis is proposed. Fuzzy logic is used to formalize expert knowledge and work with linguistic parameters such as “high load” or “low circuit flexibility”. It allows you to process blurred boundaries of criteria and build an IF–THEN rule system for architecture selection. The neural network component provides training based on historical data, identification of nonlinear patterns and adaptation to new conditions. Their integration is implemented in the form of a neuro-fuzzy model (for example, ANFIS), which combines the explicitness of the rules with the possibility of further training. The proposed architecture includes three levels: normalization and fuzzification of input data, a block of logical output with defuzzification and a corrective neural network subsystem. At the output, a probabilistic assessment of the priorities of architectural solutions is formed - relational, document–oriented, graph, column, or hybrid. Examples of rules, modeling results, and practical application scenarios are given: IP design, migration between databases, support for DevOps processes, and educational tasks. The practical significance of the research lies in reducing dependence on subjective expert assessments and increasing the reproducibility of architectural solutions. The prospects of expanding the rule base, applying deep network architectures, and integrating the model into engineering tools are emphasized.