🤖 AI Summary
Traditional relational databases struggle to process online signal data streams in medical monitoring efficiently and deterministically. To address this challenge, this work proposes the first deterministic stream processing framework tailored for medical monitoring scenarios. The framework introduces a formal algebraic system for data sequences grounded in rigorous mathematical foundations and designs stream processing operators with precise semantics. This algebraic structure is theoretically linked to Beatty’s and Fraenkel’s theorems, ensuring predictability and correctness throughout the processing pipeline. By providing formal guarantees alongside high performance and reliability, the proposed model significantly enhances the capability of medical monitoring systems to handle time-critical, continuous physiological data streams.
📝 Abstract
A data management system can be separated in typical data processing systems. Unfortunately, relational data management systems are not efficient enough to handle the on-line signal processing task in a monitoring system. The main current in research into database management system model for the needs of monitoring systems is connected with a data stream model. However, these systems are non-deterministic. This paper presents the developed methods of data stream processing for signal processing tasks in medical database management systems, as well as the developed theorems of data sequences (stream) algebra with formal proofs. A direct link between some introduced operators and Beatty and Fraenkel theorems has been proved