A flexible denormalization technique for data analysis above a deeply-structured relational database: biomedical applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F15%3A00082481" target="_blank" >RIV/00216224:14330/15:00082481 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-16483-0_12" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-16483-0_12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-16483-0_12" target="_blank" >10.1007/978-3-319-16483-0_12</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A flexible denormalization technique for data analysis above a deeply-structured relational database: biomedical applications

Popis výsledku v původním jazyce

Relational databases are sometimes used to store biomedical and patient data in large clinical or international projects. This data is inherently deeply structured, records for individual patients contain varying number of variables. When ad-hoc access to data subsets is needed, standard database access tools do not allow for rapid command prototyping and variable selection to create flat data tables. In the context of Thalamoss, an international research project on beta-thalassemia, we developed and experimented with an interactive variable selection method addressing these needs. Our newly-developed Python library sqlAutoDenorm.py automatically generates SQL commands to denormalize a subset of database tables and their relevant records, effectively generating a flat table from arbitrarily structured data. The denormalization process can be controlled by a small number of user-tunable parameters.

Název v anglickém jazyce

A flexible denormalization technique for data analysis above a deeply-structured relational database: biomedical applications

Popis výsledku anglicky

Relational databases are sometimes used to store biomedical and patient data in large clinical or international projects. This data is inherently deeply structured, records for individual patients contain varying number of variables. When ad-hoc access to data subsets is needed, standard database access tools do not allow for rapid command prototyping and variable selection to create flat data tables. In the context of Thalamoss, an international research project on beta-thalassemia, we developed and experimented with an interactive variable selection method addressing these needs. Our newly-developed Python library sqlAutoDenorm.py automatically generates SQL commands to denormalize a subset of database tables and their relevant records, effectively generating a flat table from arbitrarily structured data. The denormalization process can be controlled by a small number of user-tunable parameters.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/7E13011" target="_blank" >7E13011: THALAssaemia MOdular Stratification System for personalized therapy of beta-thalassemia</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science 9043, Bioinformatics and Biomedical Engineering, Third International Conference, IWBBIO 2015, Granada, Spain, April 15-17 2015, Proceedings, Part I

ISBN

9783319164823

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

14

Strana od-do

120-133

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Granada, Spain

Datum konání akce

15. 4. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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