FUZZY SYSTEM FOR ANALYSIS OF BUSINESS RULES OF THE SUBJECT AREA DURING DATABASE CREATION - Firsov, O. D.; Marischuk, O. V. (Systems and Technologies, 2024)
The object of research is the process of developing a fuzzy system for analysing business rules in database design, in particular, the use of fuzzy logic to process incomplete and uncertain data in modern business processes. The aim of the study is to develop a fuzzy system for analysing business rules of the subject area during database design; to develop the corresponding software using C# programming language, with the use of SQL databases to ensure adaptability and flexibility of the system when working with fuzzy data. The analytical review presents the results of the analysis of domestic and foreign literature on the use of fuzzy logic in database design, as well as existing methods of processing fuzzy and incomplete data in business systems. The review includes a comparison of different approaches to the use of fuzzy systems in the context of data management and database design. In the research part, a model of a fuzzy system for analysing and processing business rules is developed, methods for integrating fuzzy logic into the database design process are established, and the results of testing the system on real business scenarios are presented to assess its effectiveness in comparison with traditional approaches. The application part presents the results of software development using the C# programming language, which allows automating the process of analysing business rules and designing a database taking into account fuzzy and incomplete data, which increases the adaptability and accuracy of the system. As a result of the calculations, values are obtained that can be presented in the form of graphical results or specific numerical indicators indicating compliance with the specified criteria or parameters. The analysis of the results is carried out on the basis of comparing the obtained values with predefined limits and business rules. This allows you to identify anomalies or deviations from expected values, as well as determine how effectively the system works for each specific situation. For each resulting set, its validity and accuracy are checked. This may include: assessing the degree to which elements belong to a given category, determining the most significant factors affecting the result, comparing the obtained results with previous or control data to identify changes or trends.