Index Tuning

Index tuning optimizes database indexes to enhance query performance, a critical task becoming increasingly complex with the growth of big data. Current research emphasizes automated index tuning, leveraging machine learning techniques like Bayesian optimization to efficiently explore vast parameter spaces and recommend optimal index configurations, often focusing on multi-objective optimization balancing speed and accuracy. This automated approach addresses scalability challenges in modern cloud environments and aims to minimize performance regressions during deployment, ultimately improving the efficiency and usability of large-scale data management systems.