Title: Artificial Intelligence for Optimizing Binary Search Trees
Authors: in Large-Scale Data Systems
Volume: 10
Issue: 7
Pages: 1-7
Publication Date: 2026/07/28
Abstract:
The rapid growth of digital information has transformed the requirements of modern computing systems. Efficient storage, retrieval, and management of large volumes of data have become essential for organizations operating in data-intensive environments. Binary Search Trees (BSTs) remain one of the most important data structures used for searching, insertion, and deletion operations due to their simplicity and efficiency. However, the performance of BSTs depends heavily on maintaining a balanced structure. When a BST becomes unbalanced, search operations become significantly slower, reducing overall system performance. Artificial Intelligence (AI) has emerged as a powerful technology capable of learning patterns, making predictions, and supporting intelligent decision-making processes. The integration of AI techniques with traditional data structures presents new opportunities for improving system efficiency. Machine learning algorithms can analyze historical access patterns, identify frequently accessed nodes, and predict future search behavior. This research investigates the application of Artificial Intelligence for optimizing Binary Search Trees in large-scale data systems. The study examines the limitations of traditional BST implementations, reviews existing balancing techniques, and proposes a conceptual framework called SmartBST. The proposed framework combines predictive analytics with adaptive tree restructuring to improve search efficiency, scalability, and workload adaptability. The findings suggest that AI-assisted optimization has significant potential for enhancing traditional data structures and supporting efficient information retrieval in modern computing environments.