An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics.
Stud Health Technol Inform
; 295: 376-379, 2022 Jun 29.
Article
in English
| MEDLINE | ID: covidwho-1924039
ABSTRACT
Big Data has proved to be vast and complex, without being efficiently manageable through traditional architectures, whereas data analysis is considered crucial for both technical and non-technical stakeholders. Current analytics platforms are siloed for specific domains, whereas the requirements to enhance their use and lower their technicalities are continuously increasing. This paper describes a domain-agnostic single access autoscaling Big Data analytics platform, namely Diastema, as a collection of efficient and scalable components, offering user-friendly analytics through graph data modelling, supporting technical and non-technical stakeholders. Diastema's applicability is evaluated in healthcare through a predicting classifier for a COVID19 dataset, considering real-world constraints.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Diastema
/
COVID-19
Type of study:
Experimental Studies
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Stud Health Technol Inform
Journal subject:
Medical Informatics
/
Health Services Research
Year:
2022
Document Type:
Article
Affiliation country:
SHTI220743
Similar
MEDLINE
...
LILACS
LIS