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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Kemp, Gavin , López Amaya, P., Ferreira da Silva, C., Vargas-Solar, G., Ghodous, P. & Collet, C. (2016). Big Data Collections and Services for Building Intelligent Transport Applications. International Journal of Electronic Business Management. 14 (1)
Exportar Referência (IEEE)
G. Kemp et al.,  "Big Data Collections and Services for Building Intelligent Transport Applications", in Int. Journal of Electronic Business Management, vol. 14, no. 1, 2016
Exportar BibTeX
@article{kemp2016_1618466387860,
	author = "Kemp, Gavin  and López Amaya, P. and Ferreira da Silva, C. and Vargas-Solar, G. and Ghodous, P. and Collet, C.",
	title = "Big Data Collections and Services for Building Intelligent Transport Applications",
	journal = "International Journal of Electronic Business Management",
	year = "2016",
	volume = "14",
	number = "1"
}
Exportar RIS
TY  - JOUR
TI  - Big Data Collections and Services for Building Intelligent Transport Applications
T2  - International Journal of Electronic Business Management
VL  - 14
IS  - 1
AU  - Kemp, Gavin 
AU  - López Amaya, P.
AU  - Ferreira da Silva, C.
AU  - Vargas-Solar, G.
AU  - Ghodous, P.
AU  - Collet, C.
PY  - 2016
SN  - 1728-2047
AB  - This paper presents an approach for building data collections and cloud services required
for building intelligent transport applications. Services implement Big Data analytics
functions that can bring new insights and useful correlations of large data collections and
provide knowledge for managing transport issues. Applying data analytics to transport
systems brings better understanding to the transport networks revealing unexpected choking
points in cities. This facility is still largely inaccessible to small companies and citizens due
to their limited access to computational resources. A cloud service oriented architecture
opens new perspectives for democratizing the use of efficient and personalized big data
management and analytics.
ER  -