A Data Integration Architecture for Smart Cities
Resumo
The data generated by smart cities have low integration, as the systems that produce them are usually closed and developed for specific needs. Moreover, the large volume of data, and the semantic and structural changes in datasets over time make the use of data to support decision-making even more difficult. In this work, we identify the main requirements of a data integration system to support decision-making in cities, focusing on its challenges. We analyze some existing data integration solutions, to uncover their features and limitations. Based on these results, we propose a new microservice architecture to support the development of software platforms for integrating smart cities’ heterogeneous data and a guideline to assess their performance.
Palavras-chave:
Big data, smart cities, data integration
Referências
Cheng, B., Longo, S., Cirillo, F., Bauer, M., and Kovacs, E. (2015). Building a bigdata platform for smart cities: Experience and lessons from Santander. In2015 IEEEInternational Congress on Big Data, IEEE BigData 2015, pages 592–599.
Consoli, S., Mongiovic, M., Nuzzolese, A. G., Peroni, S., Presutti, V., Reforgiato Recu-pero, D., and Spampinato, D. (2015). A smart city data model based on semantics bestpractice and principles. In24th Intl. Conference on World Wide Web, WWW’15.
Costa, C. and Santos, M. Y. (2017). The SusCity big data warehousing approach for smartcities. InProceedings of the 21st International Database Engineering & ApplicationsSymposium, IDEAS 2017, page 264–273. ACM.
Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., Ahmed, E.,and Chiroma, H. (2016). The role of big data in smart city.International Journal ofInformation Management, 36(5):748-758.
Li, Z., OBrien, L., and Zhang, H. (2013). CEEM: A practical methodology for cloudservices evaluation. InIEEE 9th World Congress on Services, pages 44–51.
Li, Z., O’Brien, L., Zhang, H., and Cai, R. (2012a). A factor framework for experimentaldesign for performance evaluation of commercial cloud services. In4th IEEE Intl.Conf. on Cloud Computing Technology and Science Proceedings, pages 169–176.
Li, Z., O’Brien, L., Zhang, H., and Cai, R. (2012b). On a catalogue of metrics for eval-uating commercial cloud services. In2012 ACM/IEEE 13th International Conferenceon Grid Computing, pages 164–173.
Mehmood, H., Gilman, E., Cortes, M., Kostakos, P., Byrne, A., Valta, K., Tekes, S., andRiekki, J. (2019). Implementing big data lake for heterogeneous data sources. InIEEE35th Intl. Conference on Data Engineering Workshops (ICDEW 2019), pages 37–44.
Psyllidis, A., Bozzon, A., Bocconi, S., and Titos Bolivar, C. (2015). A platform forurban analytics and semantic data integration in city planning. InComputer-AidedArchitectural Design Futures. The Next City-New Technologies and the Future of theBuilt Environment, CAAD Futures 2015, pages 21–36. Springer Berlin Heidelberg.
Raghavan, S., Simon, B. Y. L., Lee, Y. L., Tan, W. L., and Kee, K. K. (2020). Dataintegration for smart cities: Opportunities and challenges. In Alfred, R., Lim, Y.,Haviluddin, H., and On, C. K., editors,Computational Science and Technology, pages393–403. Springer Singapore.
Rathore, M. M., Ahmad, A., Paul, A., and Rho, S. (2016). Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks, 101:63-80.
Consoli, S., Mongiovic, M., Nuzzolese, A. G., Peroni, S., Presutti, V., Reforgiato Recu-pero, D., and Spampinato, D. (2015). A smart city data model based on semantics bestpractice and principles. In24th Intl. Conference on World Wide Web, WWW’15.
Costa, C. and Santos, M. Y. (2017). The SusCity big data warehousing approach for smartcities. InProceedings of the 21st International Database Engineering & ApplicationsSymposium, IDEAS 2017, page 264–273. ACM.
Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., Ahmed, E.,and Chiroma, H. (2016). The role of big data in smart city.International Journal ofInformation Management, 36(5):748-758.
Li, Z., OBrien, L., and Zhang, H. (2013). CEEM: A practical methodology for cloudservices evaluation. InIEEE 9th World Congress on Services, pages 44–51.
Li, Z., O’Brien, L., Zhang, H., and Cai, R. (2012a). A factor framework for experimentaldesign for performance evaluation of commercial cloud services. In4th IEEE Intl.Conf. on Cloud Computing Technology and Science Proceedings, pages 169–176.
Li, Z., O’Brien, L., Zhang, H., and Cai, R. (2012b). On a catalogue of metrics for eval-uating commercial cloud services. In2012 ACM/IEEE 13th International Conferenceon Grid Computing, pages 164–173.
Mehmood, H., Gilman, E., Cortes, M., Kostakos, P., Byrne, A., Valta, K., Tekes, S., andRiekki, J. (2019). Implementing big data lake for heterogeneous data sources. InIEEE35th Intl. Conference on Data Engineering Workshops (ICDEW 2019), pages 37–44.
Psyllidis, A., Bozzon, A., Bocconi, S., and Titos Bolivar, C. (2015). A platform forurban analytics and semantic data integration in city planning. InComputer-AidedArchitectural Design Futures. The Next City-New Technologies and the Future of theBuilt Environment, CAAD Futures 2015, pages 21–36. Springer Berlin Heidelberg.
Raghavan, S., Simon, B. Y. L., Lee, Y. L., Tan, W. L., and Kee, K. K. (2020). Dataintegration for smart cities: Opportunities and challenges. In Alfred, R., Lim, Y.,Haviluddin, H., and On, C. K., editors,Computational Science and Technology, pages393–403. Springer Singapore.
Rathore, M. M., Ahmad, A., Paul, A., and Rho, S. (2016). Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks, 101:63-80.
Publicado
04/10/2021
Como Citar
RIBEIRO, Murilo Borges; BRAGHETTO, Kelly Rosa.
A Data Integration Architecture for Smart Cities. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 36. , 2021, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 205-216.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2021.17878.
