Database as a Means of Obtaining Adaptive Model of a Subject Area with Weakly Formalized Objects

The increasing dynamism of modern business environments requires the development of adaptive information systems, especially for subject areas with weakly formalized objects. Traditional static data models often prove ineffective in adequately supporting such rapidly changing conditions, which leads to significant losses in information support of the project due to the need for its processing. An innovative approach is proposed, focused on the use of databases as adaptive models of a subject area. The methodology is based on the use of databases with a flexible schema, in particular NoSQL (document and graph), which are capable of processing large volumes of rapidly changing, unstructured or semi-structured data, offering significantly greater scalability and adaptability compared to relational databases. Additionally, the principles of adaptive object models are integrated, where classes, attributes, relationships, and behavior are represented as metadata that are interpreted at runtime, allowing subject matter experts to modify the model in real time. The goal of the study is to develop an adaptive model for the subject matter «University Publishing House», which dynamically adjusts to changes in the cost of materials and their consumption rates. The problem is that traditional modeling cannot adequately reflect the inherent dynamism and poorly formalized nature of real business processes, which leads to expensive and time-consuming system modifications. The proposed mechanism implements a continuous feedback loop that automatically detects and corrects discrepancies between estimated and actual costs of consumables. This is a significant departure from traditional “rigid” models, transforming the database into a domain model as an active, self-optimizing component of an adaptive system. This approach increases flexibility, accuracy, and operational efficiency in managing complex, evolving business processes, providing continuous refinement of the system’s internal parameters and reducing the need for manual intervention.