Fuzzy Logic Algorithm for Index Optimization in Database Query

In the era of digital economy, distributed database systems play a crucial role in accommodating the surge of data resources and enabling efficient data retrieval and processing. This study investigates a novel fuzzy logic algorithm for index optimization in database queries, aiming to improve query performance and system efficiency. By reviewing current research on query optimization models and algorithms, we identify key trends and challenges in the field. The proposed methodology consists of four main steps: Query Decomposition, Data Localization, Global Optimization, and Local Optimization. This study presents a detailed examination of each step and show how they collectively minimize query cost and response time in distributed systems. Simulation and test evaluations provide insights into query response time distribution, throughput distribution, and query cost distribution. Comparative analysis between the proposed scenario and experimental results highlights the effectiveness of the proposed algorithm in improving query performance.