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Grant support

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.

Analysis of institutional authors

Alic, AsAuthorBlanquer, ICorresponding Author

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October 30, 2024
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Article

BIGSEA: A Big Data analytics platform for public transportation information

Publicated to:Future Generation Computer Systems-The International Journal Of Escience. 96 243-269 - 2019-07-01 96(), DOI: 10.1016/j.future.2019.02.011

Authors: Alic, AS; Almeida, J; Aloisio, G; Andrade, N; Antunes, N; Ardagna, D; Badia, RM; Basso, T; Blanquer, I; Braz, T; Brito, A; Elia, D; Fiore, S; Guedes, D; Lattuada, M; Lezzi, D; Maciel, M; Meira, W; Mestre, D; Moraes, R; Morais, F; Pires, CE; Kozievitch, NP; dos Santos, W; Silva, P; Vieira, M

Affiliations

- Author
BSC - Author
Fdn Ctr Euromediterraneo Cambiamenti Climat CMCC - Author
Politecn Milan - Author
Spanish Natl Res Council IIIA CSIC, Artificial Intelligence Res Inst - Author
Univ Campinas UNICAMP, Campinas - Author
Univ Coimbra, Dept Informat Engn, CISUC - Author
Univ Fed Campina Grande - Author
Univ Fed Minas Gerais, Belo Horizonte - Author
Univ Politecn Valencia, CSIC, Inst Instrumentat Mol Imaging - Author
Univ Salento - Author
Univ Tecnol Fed Parana UTFPR, Curitiba - Author
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Abstract

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.

Keywords

Big dataCloud servicesData analyticsData ingestionsDeploymentDigital storageDistributed database systemsIntensive computingModelling and analysisOpen source licenseOpen source softwareParallelisationPerformancePublic transportationQuality controlQuality of serviceScientific researchesSentiment analysisTraffic congestionWeb servicesWorkflows

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Future Generation Computer Systems-The International Journal Of Escience due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2019, it was in position 8/108, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Theory & Methods. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.08. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 1.17 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 6.33 (source consulted: Dimensions Oct 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-10-02, the following number of citations:

  • WoS: 19
  • Scopus: 29

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-10-02:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 211 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: http://hdl.handle.net/10251/144564

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Brazil; Italy; Portugal.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Alic Dadu, Andrei Stefan) .

the author responsible for correspondence tasks has been Blanquer Espert, Ignacio.