A Unified Cloud-Native Architecture For Heterogeneous Data Aggregation And Computation

Fatemeh Rouzbeh, A. Grama, Paul M. Griffin, Mohammad Adibuzzaman
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Abstract

Improving healthcare depends on collecting and analyzing different types of health related data such as Electronic Health Records (EHR), Patient Generated Health Data (PGHD), prescription and medication data and medical image data. Even though different solutions in terms of storage and processing have been designed and developed but each solution is usually designed for a specific type of data. Storing, processing, and analyzing all types of data using a single solution necessarily doesn't result in best performance and quality of analysis. To acquire the better quality, each types of data requires its own type of storage, data processing and machine learning solutions which cannot be integrated as a unified system in some cases. In order to have a unified system that serves all types of data we propose a modular cloud native architecture with autonomous modules in terms of control, deployment and management for each types of data.
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异构数据聚合与计算的统一云原生架构
改善医疗保健取决于收集和分析不同类型的健康相关数据,如电子健康记录(EHR)、患者生成的健康数据(PGHD)、处方和药物数据以及医疗图像数据。尽管在存储和处理方面已经设计和开发了不同的解决方案,但每种解决方案通常是为特定类型的数据设计的。使用单一解决方案存储、处理和分析所有类型的数据不一定会产生最佳的性能和分析质量。为了获得更好的质量,每种类型的数据都需要自己的存储类型、数据处理和机器学习解决方案,在某些情况下,这些解决方案不能集成为一个统一的系统。为了有一个统一的系统来服务所有类型的数据,我们提出了一个模块化的云原生架构,在控制、部署和管理每种类型的数据方面都有自主的模块。
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