用于智能医疗监控的医疗大数据架构

Medical big data architecture for smart medical monitoring

智能可穿戴医疗设备所生成混杂结构化和非结构化的海量数据对医疗大数据分析平台的性能提出了巨大挑战,为此文中提出了一种面向医疗物联网的大数据架构。所提出的医疗大数据架构采用了公有云技术,包括两个主要的子架构,即雾计算架构和分组选择(GC)架构。雾计算架构使用Apache Pig和Apache HBase等大数据技术收集和存储从不同传感器设备生成的海量传感数据。GC架构用于确保雾计算与公有云的高效集成。将该框架应用于基于心脏疾病预测模型的健康系统,证明了所提出的架构在实验的数据吞吐量下具有良好的CPU计算性能和数据存储性能。

The hybrid structured and unstructured massive data generated by intelligent wearable medical devices poses a huge challenge to the performance of medical big data analysis platform. Therefore,this paper proposes a big data architecture for medical Io T. The proposed medical big data architecture uses public cloud technology,including two main sub-architectures,the fog computing architecture and the packet selection( GC) architecture. The fog computing architecture uses big data technologies such as Apache Pig and Apache HBase to collect and store massive sensor data generated from different sensor devices. The GC architecture is used to ensure efficient integration of fog calculations with public clouds.Applying the framework to the health system based on the heart disease prediction model under experimental data throughput proves that the proposed architecture has good CPU computing performance and data storage performance.