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Enhancing Productivity in Database Management Through AI: A Three-Phase Approach for Database

Kushagra Parashar, Ajay Dev, Aditya Kumar, Darpan Khatri

TL;DR

This paper tackles the challenge of democratizing database analytics by introducing a three-phase AI framework for PostgreSQL that converts natural language prompts into optimized SQL, analyzes results, and automatically generates structured reports. The approach combines NL2SQL translation with schema-aware attention, iterative data analysis using anomaly detection and forecasting, and automated question generation with transformer-based summarization. Key findings include a SQL syntax accuracy of 96.2% and schema linking precision of 89%, along with substantial productivity gains such as up to 70% reduction in DBA workload and significant latency reductions across query types. The work demonstrates practical impact by delivering end-to-end automation—enabling non-technical users to obtain insights quickly while reducing operational costs and reliance on specialized DBAs; it also outlines ethical and security considerations and avenues for future enhancement in enterprise settings.

Abstract

This paper presents a novel AI-powered framework designed to streamline database management and query optimization for PostgreSQL systems. Structured in three phases: Natural Language to SQL Translation, Query Execution and Analysis, and Insight Generation, the approach empowers users with intuitive, intelligent interaction with databases. By leveraging advanced natural language processing and language models, the system enables non-technical users to extract meaningful insights from complex datasets, reducing the dependency on specialized DBAs. The framework also introduces iterative refinement of queries, automatic report generation, and support for temporal data forecasting. Experimental results demonstrate improved productivity, reduced query latency, and enhanced accuracy, validating the system's effectiveness across diverse business use cases. The solution was developed and evaluated at ABV-IIITM Gwalior, under the guidance of Prof. Dr. Arun Kuma

Enhancing Productivity in Database Management Through AI: A Three-Phase Approach for Database

TL;DR

This paper tackles the challenge of democratizing database analytics by introducing a three-phase AI framework for PostgreSQL that converts natural language prompts into optimized SQL, analyzes results, and automatically generates structured reports. The approach combines NL2SQL translation with schema-aware attention, iterative data analysis using anomaly detection and forecasting, and automated question generation with transformer-based summarization. Key findings include a SQL syntax accuracy of 96.2% and schema linking precision of 89%, along with substantial productivity gains such as up to 70% reduction in DBA workload and significant latency reductions across query types. The work demonstrates practical impact by delivering end-to-end automation—enabling non-technical users to obtain insights quickly while reducing operational costs and reliance on specialized DBAs; it also outlines ethical and security considerations and avenues for future enhancement in enterprise settings.

Abstract

This paper presents a novel AI-powered framework designed to streamline database management and query optimization for PostgreSQL systems. Structured in three phases: Natural Language to SQL Translation, Query Execution and Analysis, and Insight Generation, the approach empowers users with intuitive, intelligent interaction with databases. By leveraging advanced natural language processing and language models, the system enables non-technical users to extract meaningful insights from complex datasets, reducing the dependency on specialized DBAs. The framework also introduces iterative refinement of queries, automatic report generation, and support for temporal data forecasting. Experimental results demonstrate improved productivity, reduced query latency, and enhanced accuracy, validating the system's effectiveness across diverse business use cases. The solution was developed and evaluated at ABV-IIITM Gwalior, under the guidance of Prof. Dr. Arun Kuma

Paper Structure

This paper contains 16 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. AI in Database Automation & Query Optimization
  3. Automation in PostgreSQL
  4. Query Optimization
  5. Technical Workflow
  6. Phase 1: Data Retrieval & Query Generation
  7. Phase 2: Data Analysis & Insights
  8. Phase 3: Automated Question Generation & Summary
  9. Results & Evaluation
  10. Performance Benchmarks
  11. Accuracy Metrics
  12. Productivity Gains
  13. Challenges & Future Work
  14. Limitations
  15. Future Improvements
  16. ...and 1 more sections

Figures (1)

  • Figure 1: Flowchart.