AI-Assisted Schema Translation and Verified Migration: From Object-Relational Models to NoSQL Document-Oriented Stores

The transition from object relational databases (ORDBs) to document oriented NoSQL stores offers increased flexibility and scalability in modern data management. However, existing migration processes remain largely manual and heuristic, hindering automation, formal verification, and adaptability to evolving workloads. This paper introduces a principled AI-assisted framework that unifies schema translation and data migration for end-to-end ORDB-to-NoSQL transformation. The framework operates through a four-stage pipeline comprising:(i) database metadata extraction, (ii) prediction of mapping strategies using a dual encoder that integrates a Graph Neural Network (GNN) for structural reasoning and a Transformer for semantic interpretation, (iii) automated data migration guided by the predicted mappings, and (iv) symbolic verification ensuring semantic and structural correctness. The verification stage enforces coverage, key fidelity, referential realizability, and type soundness constraints to guarantee loss-free transformation, while an optional workload-aware component refines verified mappings for query efficiency. Experimental evaluation on the RetailDB benchmark demonstrates that the proposed framework establishes a safe, explainable, and adaptable foundation for AI-assisted schema and data migration between object relational and document oriented models.

@article{Toufik2025,
  title     = {AI-Assisted Schema Translation and Verified Migration: From Object-Relational Models to NoSQL Document-Oriented Stores},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Fouad Toufik and Abd Allah Aouragh and Abdelhak Khalil},
  doi       = {10.14569/IJACSA.2025.0161194},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161194}
}