Fatemeh Rouzbeh, A. Grama, Paul M. Griffin, Mohammad Adibuzzaman
{"title":"A Unified Cloud-Native Architecture For Heterogeneous Data Aggregation And Computation","authors":"Fatemeh Rouzbeh, A. Grama, Paul M. Griffin, Mohammad Adibuzzaman","doi":"10.1145/3388440.3414911","DOIUrl":null,"url":null,"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.","PeriodicalId":411338,"journal":{"name":"Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"62 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3388440.3414911","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.