Abstract
Defining a business intelligence project for a transportation system with more than 10 k-users per day could become a challenging problem. A transportation system like this would generate more than 400 million of registers per month when monitoring users each minute. That is why, some strategies need to be applied to the ETL process to correctly handle the data generated by big transportation systems. This paper explores different operational database (OD) architectures and analyze their impact on processing time of the ETL stage in a business intelligence. The database architectures reviewed are: one centralized OD, one logical-centralized OD and distributed OD. This model is being tested with the transportation system defined in the city of Poza Rica, Mexico. This system contains more than three million simulated registers per day and the entire ETL process is done under 136 s. This model runs on a Quad-core Intel Xeon processor 8 GB RAM OSX Yosemite 10.10.5 computer.
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This research is sponsored in part by the Mexican Agency for International Development Cooperation (AMEXCID) and the Uruguayan Agency for International Cooperation (AUCI) through the Joint Uruguay-Mexico Cooperation Fund. This research work reflects only the points of view of the authors and not those of the AMEXCID or the AUCI.
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Cristobal-Salas, A. et al. (2019). ETL Processing in Business Intelligence Projects for Public Transportation Systems. In: Torres, M., Klapp, J. (eds) Supercomputing. ISUM 2019. Communications in Computer and Information Science, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-38043-4_4
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