Optimization of Object Relational Mapping: Query Profiling Integration Based on Frequency and Timestamps Implementation in LRU

Point of Sale (POS) application using Object Relational Mapping (ORM) to bridge the gap between objectoriented programming and relational databases, enhancing productivity, performance, and system maintainability. Despite its benefits, ORM frameworks face performance overhead due to the computational costs of translating object-oriented operations into SQL queries and managing relational mappings. Limited research addresses these challenges, but recent studies suggest optimization strategies like thread division, data partitioning, in-memory mapping, and query refinement. To tackle these issues, this study proposes integrating query profiling and caching strategies by implementing timestamps within the Least Recently Used (LRU) algorithm to enhance performance and mitigate performance overhead in ORM frameworks. The proposed method, referred to as OptiORMLRU, is applied to five POS system datasets obtained from GitHub repositories. The implementation of OptiORM-LRU demonstrates superior performance characterized by enhanced performance, exhibiting reduced response times and optimized throughput. The incorporation of timestamps within the LRU caching mechanism facilitates more accurate data eviction within the ORM framework.