Extração, Integração e Importação de Dados Heterogêneos em Cidades Inteligentes: Um Mapeamento Sistemático
Resumo
Dados exercem um papel fundamental em cidades inteligentes, sendo utilizados por usuários e aplicações para os mais diferentes propósitos. Como consequência da diversidade de dispositivos e sistemas existentes e da falta de padronização em termos de modelos, protocolos e formatos de dados adotados, observa-se que os dados de cidades inteligentes sãso bastante heterogêneos. Tal heterogeneidade impõe dificuldades ao desenvolvimento de aplicações e utilização desses dados, fazendo com que seja necessário investigar estratégias e soluções para extração, integração e importação de dados em ambientes de cidades inteligentes. A fim de prover um panorama do atual estado da arte com relação a esse tópico, foi realizado um mapeamento sistemático no qual 28 estudos disponíveis na literatura foram coletados e analisados para identificar estratégias, metodologias e facilidades existentes para apoiar e implementar essas atividades. Este artigo apresenta os principais resultados do mapeamento sistemático realizado, bem como um conjunto de desafios de pesquisa e desenvolvimento que podem direcionar trabalhos futuros.
Palavras-chave:
Cidades inteligentes, Dados heterogêneos, Extração de dados, Integração de dados, Importação de dados, Mapeamento sistemático
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Sarabia-Jacome, D., Belsa, A., Palau, C. E., Esteve, M. (2018). Exploiting IoT data and smart city services for chronic obstructive pulmonary diseases risk factors monitoring. In Proceedings of the 2018 IEEE International Conference on Cloud Engineering, páginas 351-356, USA. IEEE.
Lu, Y., Misra, A., Sun, W., Wu, H. (2018). Smartphone sensing meets transport data: A collaborative framework for transportation service analytics. IEEE Transactions on MobileComputing, 17(4):945-960.
Badidi, E., Maheswaran, M. (2018). Towards a platform for urban data management, integration and processing. In Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security, páginas 299-306, Portugal. SciTePress.
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Lee, R., Park, M., HwanLee, S. (2019). An advanced IoT data collection service for data-centric smart cities. International Journal of Innovative Technology and Exploring Engineering, 8(882):323-327.
You, L., Tunçer, B., Xing, H. (2019). Harnessing multi-source data about public sentiments and activities for informed design. IEEE Transactions on Knowledge and Data Engineering, 31(2):343-356.
Mehmood, H. et al. (2019). Implementing Big Data Lake for heterogeneous data sources. In Proceedings of the 35th IEEE International Conference on Data Engineering Workshops, páginas 37-44, USA. IEEE.
Chaturvedi, K., Kolbe, T. H. (2019). Towards establishing cross-platform interoperability for sensors in smart cities. Sensors, 19(3).
d' Aquin, M., Davies, J., Motta, E. (2015). Smart cities' data: Challenges and opportunitiesfor semantic technologies. IEEE Internet Computing, 19:66-70.
de Marina, P. C. G., Alonso, A. S., Sánchez, B. V., Barca, J. M. C., Quintero, C. E. C.(2018). Towards smart public transport data: A specific process to generate data-sets containing public transport accessibility information. In Proceedings of the the Third International Conference on Universal Accessibility in the Internet of Things and Smart Environments, páginas 66-71, USA. IARIA.
Heath, T. Bizer, C. (2011). Linked Data: Evolving the Web into a global data space. Morgan & Claypool Publishers, USA.
Kitchenham, B. A. Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. techreport, Keele University/University of Durham, United Kingdom.
Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M. (2008). Systematic mapping studies in Software Engineering. In Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, páginas 68-77, United Kingdom. British Computer Society.
Silva, L., Lima, J. A., Cacho, N., Adachi, E., Lopes, F., Cavalcante, E. (2018). Integração, relacionamento e representação de dados em cidades inteligentes: Uma revisão de literatura. In Anais do 1 Workshop Brasileiro de Cidades Inteligentes, páginas 27-36, Brasil. SBC.
Souza, A. et al. (2017). A data integration approach for smart cities: The case of Natal. In Proceedings of the Third IEEE International Smart Cities Conference, USA. IEEE.
von Landesberger, T., Brodkorb, F., Roskosch, P., Andrienko, N., Andrienko, G., Kerren,A. (2016). MobilityGraphs: Visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering. IEEE Transactions on Visualization & Computer Graphics, 22(1):11-20.
Wong, J. (2013). Leveraging the general transit feed specification for efficient transitanalysis. Transportation Research Record, 2338(1):11-19.
Zhang, H. Babar, M. A. (2010). On searching relevant studies in Software Engineering. In Proceedings of the 14th International Conference on Evaluation and Assessment in Software Engineering, páginas 111-120, USA. ACM.
Montanelli, S., Castano, S., Genta, L. (2012). Multi-Web, event-centric urban information integration. In Proceedings of the 8th International Conference on Signal Image Technology and Internet Based Systems, páginas 852-859, USA. IEEE.
Bellini, P., Nesi, P., Rauch, N. (2014). Knowledge base construction process for smart-city services. In Proceedings of the 19th International Conference on Engineering of Complex Computer Systems, páginas 186-189, USA. IEEE.
Psyllidis, A., Bozzon, A., Bocconi, S., Bolivar, C. T. (2015). A platform for urban analyticsand semantic data integration in city planning. In Celani, G., Sperling, D. M., Franco, J. M.S., editores, Computer-Aided Architectural Design Futures, volume 527 de Communicationsin Computer and Information Science, páginas 21-36. Springer Berlin Heidelberg, Germany.
Consoli, S., Recupero, D. R., Mongovi, M., Presutti, V. Cataldi, G., Patatu, W. (2015). An urban fault reporting and management platform for smart cities. In Proceedings of the 24th International Conference on World Wide Web, páginas 535-540, USA. ACM.
Tan, Y., Zhang, C., Mao, Y., Qian, G. (2015). Semantic presentation and fusion framework of unstructured data in smart cites. In Proceedings of the 10th Conference on IndustrialElectronics and Applications, páginas 897-901, USA. IEEE.
Kozievitch, N. P., Almeida, L. D. A., Silva, R. D., Minetto, R. (2016). An alternative and smarter route planner for wheelchair users: Exploring open data. In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems, USA. IEEE.
Puiu, D. etal. (2016). CityPulse: Large scale data analytics framework for smart cities. IEEE Access, 4:1086-1108.
Fotopoulou, E. et al. (2016). Linked Data analytics in interdisciplinary studies: The health impact of air pollution in urban areas. IEEE Access, 4:149-164.
Souza, A. et al. (2017). A data integration approach for smart cities: The case of Natal. In Proceedings of the 2017 International Smart Cities Conference, USA. IEEE.
Yang, Z., Gupta, K., Gupta, A., Jain, R. K. (2017). A data integration framework for urban systems analysis based on geo-relationship learning. In Proceedings of the ASCE International Workshop on Computing in Civil Engineering 2017, USA. ASCE.
Beseiso, M., Al-Alwani, A., Altameem, A. (2017). An interoperable data framework to manipulate the smart city data using semantic technologies. International Journal of Advanced Computer Science and Applications, 8(8):68-72.
Komamizu, T., Nakazawa, J., Amagasa, T., Kitagawa, H., Tokuda, H. (2017). Analytical toolbox for smart city applications: Garbage collection log use case. In Proceedings of the 2017 IEEE International Conference on Big Data, páginas 4105-4110, USA. IEEE.
Yue, C. et al. (2017). Fusing location data for depression prediction. In Proceedings of the2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, USA. IEEE.
Balasubramani, B. S., Belingheri, O., Boria, E. S., Cruz, IL. F. Derrible, S., Siciliano, M.D. (2017). GUIDES: Geospatial urban infrastructure data engineering solutions. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in GeographicInformation Systems, páginas 1-4, USA. ACM.
Mejia, D., Villanueva-Rosales, N., Torres, E., Cheu, R. L. (2017). Integrating heterogeneous freight performance data for smart mobility. In Proceedings of the 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart CityInnovation, USA. IEEE.
Rodrigues, D., Boukerche, A., Silva, T. H., Loureiro, A., Villas, L. A. (2017). SMAFramework: Urban data integration framework for mobility analysis in smart cities. In Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems, páginas 227-236, USA. ACM.
Shivaprabhu, V. R., Balasubramani, B. S., Cruz, I. F. (2017). Ontology-based instance matching for geospatial urban data integration. In Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, páginas 1-8, USA. ACM.
Komamizu, T., Amagasa, T., Shaikh, S. A., Shiokawa, H., Kitagawa, H. (2017). SOLA: Stream OLAP-based analytical framework for roadway maintenance. In Proceedings of the 9th International Conference on Management of Digital EcoSystems, páginas 35-41, USA.ACM.
Fortini, P.M., Davis, C. A. (2018). Analysis, integration and visualization of urban data from multiple heterogeneous sources. In Proceedings of the 1st ACM SIGSPATIAL Workshop on Advances on Resilient and Intelligent Cities, páginas 17--26, USA. ACM.
Sarabia-Jacome, D., Belsa, A., Palau, C. E., Esteve, M. (2018). Exploiting IoT data and smart city services for chronic obstructive pulmonary diseases risk factors monitoring. In Proceedings of the 2018 IEEE International Conference on Cloud Engineering, páginas 351-356, USA. IEEE.
Lu, Y., Misra, A., Sun, W., Wu, H. (2018). Smartphone sensing meets transport data: A collaborative framework for transportation service analytics. IEEE Transactions on MobileComputing, 17(4):945-960.
Badidi, E., Maheswaran, M. (2018). Towards a platform for urban data management, integration and processing. In Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security, páginas 299-306, Portugal. SciTePress.
Huang, X., Wang, L., Yan, J., Deng, Z., Wang, S., Ma, Y. (2018). Towards building a distributed data management architecture to integrate multi-sources remote sensing big data. In Proceedings of the 20th IEEE International Conference on High Performance Computing and Communications; 1th IEEE International Conference on Smart City; 4th IEEEInternational Conference on Data Science and Systems, páginas 83-90, USA. IEEE.
Vela, B., Cavero, J. M., Cáceres, P., Cuesta, C. E. (2019). A semi-automatic data-scraping method for the public transport domain. IEEE Access, 7:105627-105637.
Lee, R., Park, M., HwanLee, S. (2019). An advanced IoT data collection service for data-centric smart cities. International Journal of Innovative Technology and Exploring Engineering, 8(882):323-327.
You, L., Tunçer, B., Xing, H. (2019). Harnessing multi-source data about public sentiments and activities for informed design. IEEE Transactions on Knowledge and Data Engineering, 31(2):343-356.
Mehmood, H. et al. (2019). Implementing Big Data Lake for heterogeneous data sources. In Proceedings of the 35th IEEE International Conference on Data Engineering Workshops, páginas 37-44, USA. IEEE.
Chaturvedi, K., Kolbe, T. H. (2019). Towards establishing cross-platform interoperability for sensors in smart cities. Sensors, 19(3).
Publicado
10/12/2020
Como Citar
ALMEIDA, João Gabriel Quaresma; SILVA, Jorge Pereira; CAVALCANTE, Everton Ranielly de Sousa; BATISTA, Thais Vasconcelos.
Extração, Integração e Importação de Dados Heterogêneos em Cidades Inteligentes: Um Mapeamento Sistemático. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 4. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 57-70.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2020.12353.
