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A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data

Giovanni Delussu, Luca Lianas, Francesca Frexia, Gianluigi Zanetti
doi: https://doi.org/10.1101/067371
Giovanni Delussu
CRS4 Pula, CA, Italy
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Luca Lianas
CRS4 Pula, CA, Italy
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Francesca Frexia
CRS4 Pula, CA, Italy
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Gianluigi Zanetti
CRS4 Pula, CA, Italy
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Abstract

This work presents a scalable data access layer, called PyEHR, intended for building data management systems for secondary use of structured heterogeneous biomedical and clinical data. PyEHR adopts openEHR formalisms to guarantee the decoupling of data descriptions from implementation details and exploits structures indexing to speed up searches. The persistence is guarantee by a driver layer with a common driver interface. Presently, are implemented the interfaces with two NoSQL DBMS: MongoDB and Elasticsearch. The scalability of PyEHR has been evaluated experimentally through two types of tests, namely constant load and constant number of records, with queries of increasing complexity on a two synthetic datasets of ten millions records each, containing very complex openEHR archetype structures, distributed on up to ten working nodes.

Footnotes

  • Giovanni.Delussu{at}crs4.it, Luca.Lianas{at}crs4.it, Francesca.Frexia{at}crs4.it, Gianluigi.Zanetti{at}crs4.it

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted August 04, 2016.
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A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data
Giovanni Delussu, Luca Lianas, Francesca Frexia, Gianluigi Zanetti
bioRxiv 067371; doi: https://doi.org/10.1101/067371
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A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data
Giovanni Delussu, Luca Lianas, Francesca Frexia, Gianluigi Zanetti
bioRxiv 067371; doi: https://doi.org/10.1101/067371

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