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Adaptive Indexing, Generative AI (GenAI), MongoDB-Oracle Integration, Dynamic Schema Optimization, Autonomous Database Tuning

  • Unique Paper ID: 180175
  • Volume: 12
  • Issue: 1
  • PageNo: 211-218
  • 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.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 1
  • PageNo: 211-218

Adaptive Indexing, Generative AI (GenAI), MongoDB-Oracle Integration, Dynamic Schema Optimization, Autonomous Database Tuning

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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