Structured Data-Based Query Analysis Processing by Deep Learning Algorithms and Meta Heuristic Optimization

The paper is concerned with the issue of query performance prediction in a database management system in the specific context of estimating query latency in interactive applications such as data visualization. It proposes a new algorithm of structured data query processing incorporating deep learning with optimization algorithms. In particular, the paper uses a multilayer convolutional graph transfer gradient encoder neural network to classify input queries and is thereafter optimized with the help of the enhanced glowworm flower pollination virtual optimization algorithm. The test methodology was done on different query types such as aggregate, join and basic queries. Experimental outcomes demonstrate a great improvement in performance measures, as the proposed method is characterized by a training accuracy of 93, an average precision of 90, and 41% decrease in query processing time on aggregate queries. The convergence was optimized at 78%, which surpasses the traditional methods on all categories tested. The system has proved to be much higher than the traditional query performance prediction techniques because it is scalable and can adapt to dynamic data volumes and query complexities. These results indicate that the application of AI-based models may significantly improve real-time query processing, especially when it comes to dynamic settings. The method has great potential for use in healthcare systems and financial industries where fast access to data is essential. The research finds the hybrid approach to deep learning and optimization algorithms as a potential solution to more efficient query performance prediction in contemporary database systems.