Schema Versioning in Temporal Fuzzy Databases: A Conceptual Framework for Evolving AI Data Management Systems

Modern AI-driven systems demand databases that handle both time (temporal data) and uncertainty (fuzzy data), yet their structure (schema) con stantly evolves. Existing schema versioning solutions fail to adequately address the complexity of simultaneously managing structural (schema), temporal (time), and semantic (fuzzy) evolution—a critical issue we define as the Tri-Evolution Challenge. This paper addresses this research gap by proposing TRIFOS (TRI evolution FOggy System), a formal conceptual framework for Schema Versioning in Temporal Fuzzy Databases, anchored on the established EFSM-Tempo model. We introduce the unique scientific challenges, particularly concerning the evolu tion of fuzzy metadata, and propose a novel architecture based on a new algebra of fuzzy/temporal Schema Change Operators (SCOs) and a Schema Version Graph (SVG). The resulting framework ensures the long-term integrity and auditabil ity of evolving, imprecise temporal data, which is crucial for building resilient AI systems and supporting regulatory compliance in domains like healthcare and finance