Integrating AI into SQL Query Processing: Challenges and Opportunities

Abstract

The integration of Artificial Intelligence (AI) into SQL query processing presents a transformative opportunity to enhance database performance, improve optimization strategies, and facilitate better decision-making in data management. This paper explores the challenges and opportunities associated with incorporating AI techniques into traditional SQL query processing systems. We begin by identifying key obstacles such as data quality, algorithm selection, and the need for effective integration frameworks. Simultaneously, we highlight the potential benefits, including automated query optimization, predictive analytics, and the enhancement of user query experiences. Through a comprehensive literature review, we analyze existing AI models and frameworks used in query processing, elucidating their effectiveness and limitations. Furthermore, we propose a hybrid model that combines rule-based systems with machine learning algorithms to optimize SQL queries dynamically. This model aims to leverage the strengths of both approaches, resulting in improved execution times and resource utilization. We conclude by discussing the implications of AI-enhanced SQL processing for database administrators and data scientists, emphasizing the need for further research to address the identified challenges and maximize the opportunities presented by this integration.