Authors: Shah Md. Tanzimul Kabir, Md. Yusuf Miah
Abstract: This paper provides a comprehensive analysis of the database design and optimization techniques for high-performance data management in the context of modern computer systems. As the data volumes increase exponentially and the need for low-latency data access becomes a necessity for businesses, the importance of database optimization techniques has never been more relevant. This study systematically reviews the recent literature from 2021 to 2026 and explores the evolution of traditional database optimization techniques towards a unified approach that includes physical design, query optimization, and indexing strategies. The research proposes a Holistic Database Optimization Framework (HDOF) that incorporates schema design, indexing strategies, query optimization, and workload-aware adaptation strategies. The analysis of the literature indicates that the recent advancements in database optimization techniques incorporate the use of enhanced indexing strategies such as B-tree indexes, hash indexes, and bitmap indexes; machine learning-based query optimization that provides a performance improvement of 2-3 times; and hardware acceleration techniques such as NVMe storage and GPU acceleration. The comparative evaluation of the recent literature from four different dimensions—query performance, storage efficiency, concurrency management, and adaptability—indicates that the workload-aware adaptation strategies provide better performance compared to traditional static database optimization techniques.
DOI: https://doi.org/10.5281/zenodo.20270385
