An Artificial Intelligence Driven Query Optimization Framework to Enhance Business Intelligence Systems

Abstract

This paper addresses the limitations of traditional Business Intelligence (BI) systems, such as high query latency, low scalability, and no predictive capabilities, by developing an AIdriven model. This research seeks to enhance the effectiveness and efficiency of BI systems overall by incorporating improved predictive analytics, real-time processing, and query optimization. The work entails building a specialized AI model and researching new approaches to predictive modeling and query optimization. The primary goal is to increase the validity and velocity of data-driven decision-making. The proposed methods are expected to increase system performance, adaptability, and scalability, whereas their applicability may be limited to the case study environments. The future will involve the framework's validation through experiments and performance measurement in order to gain prescriptive knowledge for companies applying cutting-edge BI solutions.