Development of a high-performance in-memory database architecture for intelligent video surveillance in critical patient care

Objectives This research aims to engineer a specialized, high-speed database architecture tailored for intelligent video surveillance in critical healthcare environments. The primary objective is to overcome the input/output operations per second (IOPS) bottlenecks and latency issues inherent in traditional SQL and general-purpose NoSQL systems, which impede real-time clinical decision-making. Methods We conceptualized and implemented “SubDataBase-0.91s,” a task-specific Database Management System (DBMS) residing entirely in Random Access Memory (RAM). The architecture employs a direct memory access model with periodic, asynchronous synchronization to the file system to ensure persistence. Performance was rigorously benchmarked against industry standards—Microsoft SQL Server, OracleDB, MySQL, PostgreSQL, MongoDB, and Redis—utilizing Node.js automation scripts to simulate high-velocity write/read cycles typical of video analytics streams. Results The proposed RAM-resident architecture demonstrated a dramatic reduction in data access latency. Specifically, SubDataBase-0.91s achieved a write/read speed increase of 8.6 times compared to MySQL (the slowest control) and outperformed Redis (the fastest commercial in-memory control) by a factor of 0.78 in specific surveillance-related transactional workloads. Conclusion The study confirms that stripping away universal ACID (Atomicity, Consistency, Isolation, Durability) compliance overhead in favor of a streamlined, memory-mapped architecture significantly enhances the throughput required for real-time patient monitoring. This solution provides a scalable foundation for next-generation “Smart Hospital” infrastructure.