AI-Driven Oracle Database Tuning Techniques for Academic Management Portals

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@article{1709486,
      author = {Prof. Dr. Parin Somani},
      title = {AI-Driven Oracle Database Tuning Techniques for Academic Management Portals},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {12},
      pages = {1741-1748},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709486.pdf},
      abstract = {With today's digitally-enabled academic environment, the performance of educational management systems is a critical factor that drives institutional efficiency, supports timely data access, and facilitates decision-making. Oracle Database is still among the most popular database systems running in academic portals because of its strength and scalability. Nevertheless, as student information, administrative tasks, and e-learning applications become more data-intensive, conventional database tuning methodologies tend to be insufficient in optimizing performance, especially under dynamic and complex workloads. This research paper discusses AI-based tuning methods as a novel solution to address the limitations of hand-crafted and rule-based optimization methods. By combining artificial intelligence (AI) and machine learning (ML) within Oracle's tuning process, the research seeks to illustrate how academic management portals can provide real-time query optimization, intelligent indexing, automatic anomaly detection, and dynamic resource allocation. The paper first presents an introduction to broad challenges academics institutions pose in efficiently handling vast amounts of data, particularly at times of high academic activity like admissions, publishing results, and online tests. It then examines the architectural elements of Oracle Database and determines key bottlenecks that impact efficiency. This is followed by the introduction of AI models like regression-based prediction of queries, reinforcement learning for workload allocation, and clustering to analyze query patterns. An experimental deployment is made on a simulated academic management portal with real-world query logs and datasets. The envisioned AI models are trained to identify slow-performing queries, suggest optimal indexing techniques, and forecast impending workload spikes. The performance is gauged using typical metrics like response time, CPU consumption, and query throughput. Results show remarkable system performance improvements with decreases in average query execution time and improved resource consumption. In addition, the paper addresses integration issues like model interpretability, training time, and compatibility with Oracle's built-in tuning utilities (e.g., AWR, ADDM, SQL Tuning Advisor). It also includes a comparative evaluation among manual, conventional automated, and AI-augmented tuning approaches. This study offers an end-to-end model for how academic institutions can make their database management systems AI-based. It ends with suggestions for horizontally scalable deployment, AI model continuous learning mechanisms, and the future applications of generative AI and self-healing databases in academic settings.},
      month = {June},
  }