Towards a Cloud Computing Paradigm for Big Data Analysis in Smart Cities
- Authors: Massobrio R.1, Nesmachnow S.1, Tchernykh A.2,3,4,5, Avetisyan A.3,6,5, Radchenko G.4
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Affiliations:
- Universidad de la Republica
- CICESE Research Center, Carretera Tijuana-Ensenada 3918
- Institute for System Programming of the RAS
- South Ural State University
- Moscow Institute of Physics and Technology
- Lomonosov Moscow State University
- Issue: Vol 44, No 3 (2018)
- Pages: 181-189
- Section: Article
- URL: https://journals.rcsi.science/0361-7688/article/view/176608
- DOI: https://doi.org/10.1134/S0361768818030052
- ID: 176608
Cite item
Abstract
In this paper, we present a Big Data analysis paradigm related to smart cities using cloud computing infrastructures. The proposed architecture follows the MapReduce parallel model implemented using the Hadoop framework. We analyse two case studies: a quality-of-service assessment of public transportation system using historical bus location data, and a passenger-mobility estimation using ticket sales data from smartcards. Both case studies use real data from the transportation system of Montevideo, Uruguay. The experimental evaluation demonstrates that the proposed model allows processing large volumes of data efficiently.
About the authors
R. Massobrio
Universidad de la Republica
Author for correspondence.
Email: renzom@fing.edu.uy
Uruguay, Montevideo, 11200
S. Nesmachnow
Universidad de la Republica
Email: renzom@fing.edu.uy
Uruguay, Montevideo, 11200
A. Tchernykh
CICESE Research Center, Carretera Tijuana-Ensenada 3918; Institute for System Programming of the RAS; South Ural State University; Moscow Institute of Physics and Technology
Email: renzom@fing.edu.uy
Mexico, Ensenada, BC, 22860; Moscow, 109004; Chelyabinsk, 454080; Dolgoprudny, Moscow oblast, 141701
A. Avetisyan
Institute for System Programming of the RAS; Lomonosov Moscow State University; Moscow Institute of Physics and Technology
Email: renzom@fing.edu.uy
Russian Federation, Moscow, 109004; Moscow, 119991; Dolgoprudny, Moscow oblast, 141701
G. Radchenko
South Ural State University
Email: renzom@fing.edu.uy
Russian Federation, Chelyabinsk, 454080