Leveraging Machine Learning for Optimal Object-Relational Database Mapping in Software Systems

Modern software systems, developed using object-oriented programming languages (OOPL), often rely on relational databases (RDB) for persistent storage, leading to the object-relational impedance mismatch problem (IMP). Although Object-Relational Mapping (ORM) tools like Hibernate and Django provide a layer of indirection, designing efficient application-specific data mappings remains challenging and error-prone. The selection of mapping strategies significantly influences data storage and retrieval performance, necessitating a thorough understanding of paradigms and systematic tradeoff exploration. The state-of-the-art systematic design tradeoff space exploration faces scalability issues, especially in large systems. This paper introduces a novel methodology, dubbed Leant, for learning-based analysis of tradeoffs, leveraging machine learning to derive domain knowledge autonomously, thus aiding the effective mapping of object models to relational schemas. Our preliminary results indicate a reduction in time and cost overheads associated with developing (Pareto-) optimal object-relational database schemas, showcasing Leant's potential in addressing the challenges of object-relational impedance mismatch and advancing object-relational mapping optimization and database design.