Automatic Database Index Tuning Using Machine Learning

Index tuning is considered as a powerful technique used to improve the database performance by ensuring the swift data access. Automating the index tuning by using Machine Learning (ML) algorithms will open up new research avenues to address the database performance challenges. ML also alleviates the performance tuning technique's dependency on the schema by adjusting the database to the changing business needs. The proposed research work presents a model to perform index selection by applying an ML classification algorithm on the dataset constructed by using the query's execution history. One of the proposed model's distinguishing characteristics is using the column's future usage rate as a feature in the Dataset, that makes the selectedindex to be adaptable for future query patterns.