The Role of AI in Predictive Database Performance Tuning
DOI:
https://doi.org/10.32628/CSEIT25112365Keywords:
Autonomous Database Management, AI-Driven Performance Tuning, Predictive Workload Forecasting, Automated Indexing Strategies, Anomaly DetectionAbstract
The integration of artificial intelligence into database performance tuning marks a pivotal evolution in data management practices. As traditional manual approaches by Database Administrators give way to predictive and autonomous systems, organizations are experiencing transformative benefits across multiple dimensions of database operations. AI technologies now enable workload prediction, automated indexing, anomaly detection, and resource optimization that far exceed human capabilities in both accuracy and efficiency. While challenges exist in implementing these systems—particularly regarding continuous learning requirements and legacy database integration—the trajectory toward fully autonomous database management continues to accelerate. This advancement fundamentally shifts the role of database professionals from routine maintenance to strategic data architecture and innovation, ultimately promising a future where databases self-optimize with minimal human oversight while delivering superior performance and reliability.
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