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@article{180175,
        author = {Anushka Raj Yadav and Amit Dhiman and Shubneet and Navjot Singh Talwandi},
        title = {Adaptive Indexing, Generative AI (GenAI),  MongoDB-Oracle Integration, Dynamic Schema  Optimization, Autonomous Database Tuning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {211-218},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180175},
        abstract = {This paper presents a GenAI-driven framework for adaptive indexing in hybrid MongoDB-Oracle data warehouses, addressing schema rigidity in multi-model environments. By combining MongoDB’s document model with Oracle’s rela- tional optimizations, we implement a reinforcement learning model trained on query patterns from MongoDB Atlas change streams and Oracle Autonomous Database metrics [1]. The system dynamically predicts optimal indexing strate- gies, automatically managing B-tree, hash, and vector indexes across both platforms. Evaluations demonstrate 55% reduced query latency for hybrid work- loads (JSON aggregation + SQL joins) versus static indexing, with 30% storage savings from AI-driven pruning. The framework resolves schema mismatch through real-time JSON-to-relational mapping via Oracle’s MongoDB API, while integrating Voyage AI’s embeddings for semantic indexing. Financial analytics case studies show maintained sub-200ms response times during schema evolu- tion, outperforming manual tuning by 40%. This approach enables autonomous optimization of petabyte-scale heterogeneous data ecosystems.},
        keywords = {Adaptive Indexing, Generative AI (GenAI), MongoDB-Oracle Integration, Dynamic Schema Optimization, Autonomous Database Tuning},
        month = {May},
        }