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@article{180155,
        author = {Shubneet and Amit Dhiman and Anushka Raj Yadav and Navjot Singh Talwandi},
        title = {GenAI-Powered Multi-Model Query Optimization: Bridging SQL, JSON, and Vector Search in Oracle-MongoDB Ecosystems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {347-353},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180155},
        abstract = {This paper introduces a neural architecture for cross-database query optimization, leveraging generative AI to bridge SQL (Oracle), JSON (MongoDB), and vector search operations. Our transformer-based model, trained on execution plans from Oracle Autonomous Database and MongoDB Atlas, demonstrates 40–65% latency reduction for hybrid queries combining SQL joins, JSON aggregations, and semantic vector searches compared to manual optimization. The system dynamically selects execution pathways using real-time workload analysis inspired by Gemini for Databases recommendations [1], while resolving schema mismatches through AI-driven JSON-to-relational mapping. By integrating Oracle 23ai’s vector indexing with MongoDB’s native vector search, we achieve sub-100ms response times for complex analytical workloads. Security constraints from Oracle’s ethical AI governance are preserved through differential privacy in query translation. Evaluation shows 2.7× improved resource utilization in hybrid cloud deployments, with vector search recall rates exceeding 92% on TPC- H benchmarks. This approach enables intent-driven data interaction patterns while abstracting database heterogeneity, advancing GenAI-enhanced data mesh architectures.},
        keywords = {GenAI, Multi-Model Query Optimization, Vector Search, Hybrid Database, Data Mesh},
        month = {May},
        }