Serverless OpenHealth at data commons scale—traversing the 20 million patient records of New York’s SPARCS dataset in real-time
In a previous report, we explored the serverless OpenHealth approach to the Web as a Global Compute space. That approach relies on the modern browser full stack, and, in particular, its configuration for application assembly by code injection. The opportunity, and need, to expand this approach has s...
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| Online Access: | https://peerj.com/articles/6230.pdf |
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doaj-f60e66bef2374ebda7953aca4bf97d2d2020-11-25T00:54:43ZengPeerJ Inc.PeerJ2167-83592019-01-017e623010.7717/peerj.6230Serverless OpenHealth at data commons scale—traversing the 20 million patient records of New York’s SPARCS dataset in real-timeJonas S. Almeida0Janos Hajagos1Joel Saltz2Mary Saltz3Biomedical Informatics, State University of New York at Stony Brook, Stony Brook, NY, United States of AmericaBiomedical Informatics, State University of New York at Stony Brook, Stony Brook, NY, United States of AmericaBiomedical Informatics, State University of New York at Stony Brook, Stony Brook, NY, United States of AmericaRadiology, State University of New York at Stony Brook, Stony Brook, NY, United States of AmericaIn a previous report, we explored the serverless OpenHealth approach to the Web as a Global Compute space. That approach relies on the modern browser full stack, and, in particular, its configuration for application assembly by code injection. The opportunity, and need, to expand this approach has since increased markedly, reflecting a wider adoption of Open Data policies by Public Health Agencies. Here, we describe how the serverless scaling challenge can be achieved by the isomorphic mapping between the remote data layer API and a local (client-side, in-browser) operator. This solution is validated with an accompanying interactive web application (bit.ly/loadsparcs) capable of real-time traversal of New York’s 20 million patient records of the Statewide Planning and Research Cooperative System (SPARCS), and is compared with alternative approaches. The results obtained strengthen the argument that the FAIR reproducibility needed for Population Science applications in the age of P4 Medicine is particularly well served by the Web platform.https://peerj.com/articles/6230.pdfServerless computingOpenhealthSparcsPublic healthEpidemiology data commons |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Jonas S. Almeida Janos Hajagos Joel Saltz Mary Saltz |
| spellingShingle |
Jonas S. Almeida Janos Hajagos Joel Saltz Mary Saltz Serverless OpenHealth at data commons scale—traversing the 20 million patient records of New York’s SPARCS dataset in real-time PeerJ Serverless computing Openhealth Sparcs Public health Epidemiology data commons |
| author_facet |
Jonas S. Almeida Janos Hajagos Joel Saltz Mary Saltz |
| author_sort |
Jonas S. Almeida |
| title |
Serverless OpenHealth at data commons scale—traversing the 20 million patient records of New York’s SPARCS dataset in real-time |
| title_short |
Serverless OpenHealth at data commons scale—traversing the 20 million patient records of New York’s SPARCS dataset in real-time |
| title_full |
Serverless OpenHealth at data commons scale—traversing the 20 million patient records of New York’s SPARCS dataset in real-time |
| title_fullStr |
Serverless OpenHealth at data commons scale—traversing the 20 million patient records of New York’s SPARCS dataset in real-time |
| title_full_unstemmed |
Serverless OpenHealth at data commons scale—traversing the 20 million patient records of New York’s SPARCS dataset in real-time |
| title_sort |
serverless openhealth at data commons scale—traversing the 20 million patient records of new york’s sparcs dataset in real-time |
| publisher |
PeerJ Inc. |
| series |
PeerJ |
| issn |
2167-8359 |
| publishDate |
2019-01-01 |
| description |
In a previous report, we explored the serverless OpenHealth approach to the Web as a Global Compute space. That approach relies on the modern browser full stack, and, in particular, its configuration for application assembly by code injection. The opportunity, and need, to expand this approach has since increased markedly, reflecting a wider adoption of Open Data policies by Public Health Agencies. Here, we describe how the serverless scaling challenge can be achieved by the isomorphic mapping between the remote data layer API and a local (client-side, in-browser) operator. This solution is validated with an accompanying interactive web application (bit.ly/loadsparcs) capable of real-time traversal of New York’s 20 million patient records of the Statewide Planning and Research Cooperative System (SPARCS), and is compared with alternative approaches. The results obtained strengthen the argument that the FAIR reproducibility needed for Population Science applications in the age of P4 Medicine is particularly well served by the Web platform. |
| topic |
Serverless computing Openhealth Sparcs Public health Epidemiology data commons |
| url |
https://peerj.com/articles/6230.pdf |
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