BIGSEA: A Big Data analytics platform for public transportation information

• Published in: Future generation computer systems 2019-07, Vol.96, p.243-269
• Main Authors: Alic, Andy S., Almeida, Jussara, Aloisio, Giovanni, Andrade, Nazareno, Antunes, Nuno, Ardagna, Danilo, Badia, Rosa M., Basso, Tania, Blanquer, Ignacio, Braz, Tarciso, Brito, Andrey, Elia, Donatello, Fiore, Sandro, Guedes, Dorgival, Lattuada, Marco, Lezzi, Daniele, Maciel, Matheus, Meira, Wagner, Mestre, Demetrio, Moraes, Regina, Morais, Fabio, Pires, Carlos Eduardo, Kozievitch, Nádia P., Santos, Walter dos, Silva, Paulo, Vieira, Marco
• Format: Article
• Language: English
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2019.02.011

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/). •Vertical and horizontal elasticity are combined to meet a given execution deadline.•Visual programming models and inherent parallelism for data analytics.•Applications for demonstrating capabilities in public transportation analysis.•Enhancement of privacy through policies.