Alic, AS.; Almeida, J.; Aloisio, G.; Andrade, N.; Antunes, N.; Ardagna, D.; Badía, R.... (2019). BIGSEA: A Big Data analytics platform for public transportation information. Future Generation Computer Systems. 96:243-269. https://doi.org/10.1016/j.future.2019.02.011
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/144564
Título:
|
BIGSEA: A Big Data analytics platform for public transportation information
|
Autor:
|
Alic, Andrei Stefan
Almeida, Jussara
Aloisio, Giovanni
Andrade, Nazareno
Antunes, Nuno
Ardagna, Danilo
Badía, R.
Basso, Tania
Blanquer Espert, Ignacio
Braz, Tarciso
Brito, A.
Elia, Donatello
Fiore, Sandro
Guedes, Dorgival
Lattuada, Marco
|
Entidad UPV:
|
Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Universitat Politècnica de València. Instituto de Instrumentación para Imagen Molecular - Institut d'Instrumentació per a Imatge Molecular
|
Fecha difusión:
|
|
Resumen:
|
[EN] Analysis of public transportation data in large cities is a challenging problem. Managing data ingestion, data storage, data quality enhancement, modelling and analysis requires intensive computing and a non-trivial ...[+]
[EN] Analysis of public transportation data in large cities is a challenging problem. Managing data ingestion, data storage, data quality enhancement, modelling and analysis requires intensive computing and a non-trivial amount of resources. In EUBra-BIGSEA (Europe¿Brazil Collaboration of Big Data Scientific Research Through Cloud-Centric Applications) we address such problems in a comprehensive and integrated way. EUBra-BIGSEA provides a platform for building up data analytic workflows on top of elastic cloud services without requiring skills related to either programming or cloud services. The approach combines cloud orchestration, Quality of Service and automatic parallelisation on a platform that includes a toolbox for implementing privacy guarantees and data quality enhancement as well as advanced services for sentiment analysis, traffic jam estimation and trip recommendation based on estimated crowdedness. All developments are available under Open Source licenses (http://github.org/eubr-bigsea, https://hub.docker.com/u/eubrabigsea/).
[-]
|
Palabras clave:
|
Performance
,
Deployment
,
Workflows
|
Derechos de uso:
|
Reserva de todos los derechos
|
Fuente:
|
Future Generation Computer Systems. (issn:
0167-739X
)
|
DOI:
|
10.1016/j.future.2019.02.011
|
Editorial:
|
Elsevier
|
Versión del editor:
|
https://doi.org/10.1016/j.future.2019.02.011
|
Código del Proyecto:
|
info:eu-repo/grantAgreement/EC/H2020/690116/EU/EUrope-BRAzil Collaboration on BIG Data Scientific REsearch through Cloud-Centric Applications/
info:eu-repo/grantAgreement/GVA//APE%2F2016%2F012/
|
Agradecimientos:
|
The work shown in this article has been funded jointly by the European Commission under the Cooperation Programme, Horizon 2020 grant agreement No 690116 (EUBra-BIGSEA) and the Ministério de Ciência, Tecnologia e Inovação ...[+]
The work shown in this article has been funded jointly by the European Commission under the Cooperation Programme, Horizon 2020 grant agreement No 690116 (EUBra-BIGSEA) and the Ministério de Ciência, Tecnologia e Inovação (MCTI) from Brazil.
[-]
|
Tipo:
|
Artículo
|