@article{0f1b9d71340b4ea3ae8975dccca0ae41,
title = "Towards a Cloud Computing Paradigm for Big Data Analysis in Smart Cities",
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. {\textcopyright} 2018, Pleiades Publishing, Ltd.",
keywords = "big data, cloud computing, intelligent transportation systems, smart cities, Cloud computing, Data handling, Information analysis, Intelligent systems, Quality of service, Smart cards, Smart city, Cloud computing infrastructures, Experimental evaluation, Hadoop frameworks, Intelligent transportation systems, Mobility estimation, Proposed architectures, Public transportation systems, Transportation system, Big data",
author = "R. Massobrio and S. Nesmachnow and A. Tchernykh and A. Avetisyan and G. Radchenko",
note = "Cited By :16 Export Date: 27 August 2021 Correspondence Address: Massobrio, R.; Universidad de la RepublicaUruguay; email: renzom@fing.edu.uy Funding details: 178415 Funding details: Agencia Nacional de Investigaci{\'o}n e Innovaci{\'o}n, ANII Funding details: Consejo Nacional de Ciencia y Tecnolog{\'i}a, CONACYT Funding details: Government Council on Grants, Russian Federation Funding text 1: 7. ACKNOWLEDGMENT The work of S. Nesmachnow and R. Massobrio is partly funded by ANII and PEDECIBA, Uruguay. This work is partially supported by Government of the Russian Federation, Act 211, contract no. 02.A03.21.0011, and CONACYT (Consejo Nacional de Ciencia y Tec-nologia,Mexico), grant no. 178415. Datasets used in this paper are from Intendencia de Montevideo.",
year = "2018",
doi = "10.1134/S0361768818030052",
language = "English",
volume = "44",
pages = "181--189",
number = "3",
}