Predictive Analytics for Autonomous Database Scaling in AI-Powered Smart Cities
Abstract
Predictive analysis plays a crucial role in optimising autonomous database scaling in AI-powered smart cities, ensuring efficient resource allocation, real-time adaptability, and seamless data processing. The rapid growth of urban data makes it compulsory to use advance machine learning algorithms in statistical models to predict workload fluctuation and prevent computational resources. The objective of this paper is to explore the integration of predictive models which includes time-series forecasting, reinforcement learning, and deep learning-based anomaly detection, which is used to enhance database elasticity, minimize latency, and optimize storage utilization.
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