Harnessing FSQL Features for Fuzzy Relational Data Model

This paper focuses on the development of Fuzzy SQL to overcome the limitations of classical database systems in handling imprecise and uncertain data. The proposed comprehensive approach enriches the capabilities of FSQL by efficiently bridging the gap between fuzzy relational data models and their respective practical implementations in databases. This approach introduces new types of fuzzy comparators, fuzzy attribute types, and fuzzy constant types in FSQL, allowing for the specification of more accurate and expressive queries. We introduce adaptive fulfillment thresholds along with fuzzy set operators in order to allow complex manipulations of fuzzy data. This paper also discusses the inclusion of FuzzyEER principles within FSQL in order to allow a seamless transformation from conceptual modeling to query language execution. The significant enhancements include the development of fuzzy functions for data manipulation, the extension of DDL to support fuzzy data types and constraints, and the introduction of special fuzzy time comparators. These extensions significantly increase the expressiveness of queries, the precision of data representation, and the handling of uncertain temporal information. Various performance evaluations have indeed shown an improvement in retrieval precision and increased user satisfaction compared to standard SQL, especially for queries involving fuzzy conditions. The improvements in FSQL create a solid foundation for the management of imprecise data within relational database systems, opening new viewpoints on applications related to decision support systems and artificial intelligence. This paper contributes to the developing area of fuzzy database systems by providing practical methodologies for the acquisition and retrieval of imprecise information in today's data-driven environment.