Os desafios e soluções para a implementação de Big Data Analytics em cidades inteligentes
Resumo
Nos últimos anos, o crescimento das cidades inteligentes tem exigido soluções para a gerência de dados urbanos massivos e heterogêneos com o objetivo de transformar dados brutos em informação. Essas soluções precisam lidar com caracterı́sticas particulares de acesso, formato e manipulação dos dados, requerendo abordagens distintas para o processamento, integração, análise de dados. Neste trabalho, discutimos os aspectos envolvidos na construção de Big Data Analytics em cidades inteligentes, ressaltando as caracterı́sticas das soluções disponı́veis. No intuito de demonstrar as ferramentas discutidas neste trabalho, foi realizado um experimento inicial, que construiu uma pipeline de dados voltada para a análise do tráfego dos ônibus da cidade de São Paulo.
Palavras-chave:
Aplicações/pipelines de ciência de dados, Processamento de dados de sensores, bancos de dados de fluxo e IOT em dados
Referências
Ahad, M. A., Paiva, S., Tripathi, G., and Feroz, N. (2020). Enabling technologies and sustainable smart cities. Sustainable cities and society, 61:102301.
Alablani, I. and Alenazi, M. (2020). EDTD-SC: An IoT sensor deployment strategy for smart cities. sensors, 20(24):7191.
Chang, X. and Cui, H. (2021). Distributed storage strategy and visual analysis for economic big data. Journal of Mathematics, 2021:3224190.
Chen, M., Mao, S., and Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2):171–209.
Clarindo, J. P., Castro, J. P. d. C., and Aguiar, C. D. d. (2021). Combining fog and cloud computing to support spatial analytics in smart cities. Journal of Information and Data Management-JIDM, 12(4):342–360.
de Carvalho Castro, J. P., Chaves Carniel, A., and Dutra de Aguiar Ciferri, C. (2020). Analyzing spatial analytics systems based on hadoop and spark: A user perspective. Software: Practice and Experience, 50(12):2121–2144.
Hai, R., Quix, C., and Jarke, M. (2021). Data lake concept and systems: a survey. CoRR, abs/2106.09592.
Liu, S., Peng, L., Chi, T., and Wang, X. (2016). Research on multi-source heterogeneous data collection for the smart city public information platform. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pages 623–626. IEEE.
Massobrio, R., Nesmachnow, S., Tchernykh, A., Avetisyan, A., and Radchenko, G. (2018). Towards a cloud computing paradigm for big data analysis in smart cities. Programming and Computer Software, 44(3):181–189.
Mătăcuţă, A. and Popa, C. (2018). Big data analytics: Analysis of features and performance of big data ingestion tools. Informatica Economica, 22(2).
Meehan, J., Aslantas, C., Zdonik, S., Tatbul, N., and Du, J. (2017). Data ingestion for the connected world. In CIDR, volume 17, pages 8–11.
Panwar, A. and Bhatnagar, V. (2020). Scrutinize the idea of hadoop-based data lake for big data storage. Applications of Machine Learning, pages 365–391.
Pereira, D. A., Ourique de Morais, W., and Pignaton de Freitas, E. (2018). Nosql real-time database performance comparison. International Journal of Parallel, Emergent and Distributed Systems, 33(2):144–156.
Rathore, M. M., Paul, A., Hong, W.-H., Seo, H., Awan, I., and Saeed, S. (2018). Exploiting iot and big data analytics: Defining smart digital city using real-time urban data. Sustainable cities and society, 40:600–610.
Reinsel, D., Gantz, J., and Rydning, J. (2017). Data age 2025: The evolution of data to life-critical. Don’t Focus on Big Data, 2.
Veiga, J., Expósito, R. R., Pardo, X. C., Taboada, G. L., and Tourifio, J. (2016). Performance evaluation of big data frameworks for large-scale data analytics. In 2016 IEEE International Conference on Big Data (Big Data), pages 424–431. IEEE.
Alablani, I. and Alenazi, M. (2020). EDTD-SC: An IoT sensor deployment strategy for smart cities. sensors, 20(24):7191.
Chang, X. and Cui, H. (2021). Distributed storage strategy and visual analysis for economic big data. Journal of Mathematics, 2021:3224190.
Chen, M., Mao, S., and Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2):171–209.
Clarindo, J. P., Castro, J. P. d. C., and Aguiar, C. D. d. (2021). Combining fog and cloud computing to support spatial analytics in smart cities. Journal of Information and Data Management-JIDM, 12(4):342–360.
de Carvalho Castro, J. P., Chaves Carniel, A., and Dutra de Aguiar Ciferri, C. (2020). Analyzing spatial analytics systems based on hadoop and spark: A user perspective. Software: Practice and Experience, 50(12):2121–2144.
Hai, R., Quix, C., and Jarke, M. (2021). Data lake concept and systems: a survey. CoRR, abs/2106.09592.
Liu, S., Peng, L., Chi, T., and Wang, X. (2016). Research on multi-source heterogeneous data collection for the smart city public information platform. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pages 623–626. IEEE.
Massobrio, R., Nesmachnow, S., Tchernykh, A., Avetisyan, A., and Radchenko, G. (2018). Towards a cloud computing paradigm for big data analysis in smart cities. Programming and Computer Software, 44(3):181–189.
Mătăcuţă, A. and Popa, C. (2018). Big data analytics: Analysis of features and performance of big data ingestion tools. Informatica Economica, 22(2).
Meehan, J., Aslantas, C., Zdonik, S., Tatbul, N., and Du, J. (2017). Data ingestion for the connected world. In CIDR, volume 17, pages 8–11.
Panwar, A. and Bhatnagar, V. (2020). Scrutinize the idea of hadoop-based data lake for big data storage. Applications of Machine Learning, pages 365–391.
Pereira, D. A., Ourique de Morais, W., and Pignaton de Freitas, E. (2018). Nosql real-time database performance comparison. International Journal of Parallel, Emergent and Distributed Systems, 33(2):144–156.
Rathore, M. M., Paul, A., Hong, W.-H., Seo, H., Awan, I., and Saeed, S. (2018). Exploiting iot and big data analytics: Defining smart digital city using real-time urban data. Sustainable cities and society, 40:600–610.
Reinsel, D., Gantz, J., and Rydning, J. (2017). Data age 2025: The evolution of data to life-critical. Don’t Focus on Big Data, 2.
Veiga, J., Expósito, R. R., Pardo, X. C., Taboada, G. L., and Tourifio, J. (2016). Performance evaluation of big data frameworks for large-scale data analytics. In 2016 IEEE International Conference on Big Data (Big Data), pages 424–431. IEEE.
Publicado
25/09/2023
Como Citar
F. VASCONCELOS, Felipe; T. RAMOS, Vinicius; J. COUTINHO, Fábio.
Os desafios e soluções para a implementação de Big Data Analytics em cidades inteligentes. In: WORKSHOP DE TRABALHOS DE ALUNOS DA GRADUAÇÃO (WTAG) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 38. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 50-56.
DOI: https://doi.org/10.5753/sbbd_estendido.2023.233368.
