BIGSEA: A Big Data analytics platform for public transportation information

被引：19

作者：

Alic, Andy S. ^{[1
]}

Almeida, Jussara ^{[2
]}

Aloisio, Giovanni ^{[3
,11
]}

Andrade, Nazareno ^{[4
]}

Antunes, Nuno ^{[5
]}

Ardagna, Danilo ^{[6
]}

Badia, Rosa M. ^{[7
,10
]}

Basso, Tania ^{[8
]}

Blanquer, Ignacio ^{[1
]}

Braz, Tarciso ^{[4
]}

Brito, Andrey ^{[4
]}

Elia, Donatello ^{[3
,11
]}

Fiore, Sandro ^{[3
]}

Guedes, Dorgival ^{[2
]}

Lattuada, Marco ^{[6
]}

Lezzi, Daniele ^{[7
]}

Maciel, Matheus ^{[4
]}

Meira Jr, Wagner ^{[2
]}

Mestre, Demetrio ^{[4
]}

Moraes, Regina ^{[8
]}

Morais, Fabio ^{[4
]}

Pires, Carlos Eduardo ^{[4
]}

Kozievitch, Nadia P. ^{[9
]}

dos Santos, Walter ^{[2
]}

Silva, Paulo ^{[5
]}

Vieira, Marco ^{[5
]}

机构：

[1] Univ Politecn Valencia, CSIC, Inst Instrumentat Mol Imaging, Valencia, Spain

[2] Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil

[3] Fdn Ctr Euromediterraneo Cambiamenti Climat CMCC, Lecce, Italy

[4] Univ Fed Campina Grande, Campina Grande, Brazil

[5] Univ Coimbra, Dept Informat Engn, CISUC, Coimbra, Portugal

[6] Politecn Milan, Milan, Italy

[7] BSC, Barcelona, Spain

[8] Univ Campinas UNICAMP, Campinas, SP, Brazil

[9] Univ Tecnol Fed Parana UTFPR, Curitiba, Parana, Brazil

[10] Spanish Natl Res Council IIIA CSIC, Artificial Intelligence Res Inst, Barcelona, Spain

[11] Univ Salento, Lecce, Italy

来源：

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2019年 / 96卷

基金：

欧盟地平线“2020”;

关键词：

PERFORMANCE; DEPLOYMENT; WORKFLOWS;

D O I：

10.1016/j.future.2019.02.011

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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 nontrivial 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/). (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：243 / 269

页数：27

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