Published Paper Details:

The rapid expansion of big data has led to significant challenges in data processing, requiring efficient and intelligent optimization techniques to enhance performance. Traditional data processing methods often struggle with high computational complexity, scalability issues, and real-time data handling. This paper presents an intelligent optimization technique designed to improve data processing performance in big data environments. The proposed approach integrates machine learning-based optimization, heuristic algorithms, and distributed computing frameworks to enhance data retrieval, storage efficiency, and processing speed. By leveraging intelligent resource allocation, task scheduling, and adaptive learning mechanisms, the system dynamically adjusts to workload variations, reducing latency and improving throughput. Experimental results demonstrate that the intelligent optimization technique significantly enhances processing efficiency compared to conventional methods. The framework achieves better performance in terms of execution time, fault tolerance, and energy efficiency, making it a viable solution for large-scale data-intensive applications. This research work emphasis in the domain of big data analytics that offers some key features like robust, scalable, and intelligent optimization model that enhances decision-making and overall system performance. Future work will explore further refinements and integration with emerging technologies such as edge computing and federated learning for enhanced real-time data processing.