Universal Metadata Dictionary: A Platform-Agnostic Metadata Representation Model for Cross-RDBMS Schema Drift Detection

Abstract

Enterprise information systems deployed in large-scale organisations commonly operate across heterogeneous Relational Database Management System (RDBMS) environments, where metadata heterogeneity between platforms prevents consistent automated schema drift detection. This paper proposes the Universal Metadata Dictionary (UMD), a platform-agnostic metadata representation model that normalises database schema metadata from six widely deployed RDBMS platforms—Oracle, Tibero, SQL Server, PostgreSQL, MySQL, and SQLite—into a unified 29-element relational structure. The UMD is governed by four design principles—platform independence, completeness, normalisation, and hashability—implemented through a five-stage pipeline: extraction, parsing, mapping, enrichment, and storage. A structured data type normalisation mapping across seven canonical categories eliminates false drift reports arising from platform-specific type nomenclature. Validated through expert review by 13 domain practitioners using a five-point Likert scale (composite mean scores: 4.52–4.70 out of 5) and structural completeness assessment confirming all 29 elements are extractable from standard JDBC and system catalogue access on all six platforms. A suite of 15 structured test scenarios defines the empirical evaluation plan spanning fault injection tests, performance benchmarks, and cross-platform type mapping validation. Three novelties distinguish the UMD from all existing approaches: (1) the first cross-RDBMS metadata normalisation model covering all six platforms simultaneously, including Tibero; (2) a per-object O(1) hash comparison mechanism enabling O(n) total Phase-1 change detection; and (3) a metadata model directly supporting AI-based Semantic Code-Schema Analysis.