Uma Arquitetura de Microsserviços para Análise de Dados de Mobilidade Urbana baseada em Dados Heterogêneos
Abstract
Data gathered by sensors, cameras, social networks and apps can contribute to the automatic detection of unusual traffic events. Furthermore, the heterogeneous nature of data sources brings the advantage of information redundancy, which can increase the degree of reliability of a detected event. This work extends the implementation of a framework for detecting anomalous traffic events by using a microservices architecture. For this, the framework was decomposed in microservices to collect data, filter and group them as time series, detect anomalies and issue real-time alerts. As a case study, the proposed architecture is used to detect anomalies in real-time urban mobility data from the city of Vitória-ES, based on data from the city hall and Twitter.
References
de Souza, A. M., Botega, L. C., Garcia, I. C., and Villas, L. A. (2018). Por aqui é mais seguro: Melhorando a mobilidade e a segurança nas vias urbanas. In XXXVI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, Porto Alegre, RS, Brasil. SBC.
Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A. Y., Foufou, S., and Bouras, A. (2014). A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE transactions on emerging topics in computing, 2(3):267–279.
Montori, F., Bedogni, L., and Bononi, L. (2017). A collaborative internet of things arIEEE Internet of Things chitecture for smart cities and environmental monitoring. Journal, 5(2):592–605.
Pan, B., Zheng, Y., Wilkie, D., and Shahabi, C. (2013). Crowd sensing of traffic anomalies based on human mobility and social media. In GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, pages 334–343.
Saha, P., Beltre, A., Uminski, P., and Govindaraju, M. (2018). Evaluation of docker containers for scientific workloads in the cloud. In Proceedings of the Practice and Experience on Advanced Research Computing, pages 1–8.
Sidauruk, A. and Ikmah (2018). Congestion correlation and classification from twitter and waze map using artificial neural network. In International Conference on Information Technology, Information System and Electrical Engineering, pages 224–229.
Silva, T. H., Celes, C., Neto, J., Mota, V., Cunha, F., Ferreira, A., Ribeiro, A., Vaz de Melo, P., Almeida, J., and Loureiro, A. (2016). Users in the urban sensing process: Challenges and research opportunities. Academic Press.
Thome, M., Neves, A., Gomes, R., and Mota, V. (2020). Um arcabouço para detecção e alerta de anomalias de mobilidade urbana em tempo real. In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, volume XXXVIII, pages 1–14.
