IEEE Independent Researcher / IEEE Senior Member, Atlanta, USA

Title of the Talk :
Intelligent Database Indexing: Optimizing Data Retrieval with AI

Abstract of the Talk:
Database indexing is one of the most important techniques for speeding up data retrieval, but managing indexes in large and frequently changing systems can be complex. As datasets grow and query patterns evolve, traditional indexing approaches like B trees, hash, bitmap, composite and covering indexes can become less effective. Static indexing strategies often fail to keep up with changing workloads, leading to slow queries, wasted storage and higher maintenance efforts.

This session explores how indexing can be taken to the next level by combining traditional methods with AI driven optimization. We will discuss how AI and machine learning can automatically analyze query workloads, predict which indexes will give the best performance and suggest or even create new indexes without manual intervention. AI can also continuously monitor index health, detect unused or redundant indexes and alert teams to potential performance bottlenecks before they impact production.

Through real world examples, attendees will see how intelligent indexing strategies can dramatically reduce query latency, improve database efficiency and lower storage costs. By embedding AI powered indexing into database management, organizations can achieve self tuning databases that adapt automatically to evolving workloads, ensuring consistent and reliable performance with minimal human effort.

By the end of this session, participants will have practical insights into building smarter, self optimizing database systems that combine the reliability of traditional indexing with the adaptability of AI.