IndexAI: AI Based Index Selection for NoSQL Databases

Mohammad Mahdi Khosravi, Pinar Karagoz, Ismail Hakki Toroslu

Abstract: In the big data era, automated index selection and recommendation has been an important research problem to improve the data access efficiency. Previous efforts on artificial intelligence based database index selection have focused on relational databases. In this work, we consider the automated index selection for NoSQL databases and investigate the feasibility of supervised learning and reinforcement learning based solutions. The experiments conducted on the YCSB dataset show that reinforcement learning improves index selection performance as in relational databases, and supervised learning gives promising results and can be considered applicable under sufficient amount of training data.