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

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.

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.

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.

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.