Title: Large-Scale Data Processing AI-Driven Adaptive Sorting Techniques
Authors: Nesreen S. Aljerjawi and Samy S. Abu-Naser
Volume: 9
Issue: 8
Pages: 99-105
Publication Date: 2025/08/28
Abstract:
The unprecedented explosion of data in modern applications, ranging from scientific simulations and financial markets to social media analytics and IoT devices, has rendered traditional, static sorting algorithms increasingly inefficient and resource-intensive. This research delves into the transformative potential of integrating Artificial Intelligence (AI) methodologies, particularly Machine Learning (ML) and Deep Learning (DL) paradigms, to engineer highly adaptive sorting techniques specifically tailored for large-scale data processing environments. The core premise is to empower sorting algorithms with the intelligence to dynamically infer, analyze, and adapt to the intrinsic characteristics of input datasets, such as their size, distribution, pre-sortedness, and real-time streaming properties. This adaptability aims to significantly enhance algorithmic performance, manifested in reduced execution times, optimized computational resource utilization, and improved scalability. The study will thoroughly review the limitations of conventional sorting approaches in the context of big data, propose a novel methodological framework for embedding AI-driven decision-making within sorting processes, and rigorously evaluate the efficacy of the proposed techniques through comprehensive simulations, comparative analyses against established benchmarks, and potentially real-world case studies to demonstrate their practical applicability and superior efficiency.