Deep Q-Learning for ETL Optimization in Heterogeneous Data Environments

This ArXiv paper likely explores the application of Deep Q-Learning (DQL) to improve the efficiency of Extract, Transform, Load (ETL) processes within diverse data environments. The use of DQL suggests an attempt to automate and optimize ETL scheduling dynamically, potentially leading to improved performance.

• •Applies Deep Q-Learning to ETL scheduling.
• •Targets heterogeneous data environments.
• •Aims to optimize ETL processes.