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English Information

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Authors
# Name
1 Jânio Lima(janio.lima@aluno.cefet-rj.br)
2 Rebecca Salles(rebeccapsalles@acm.org)
3 Cristiane Gea(cristiane.gea@@aluno.cefet-rj.br)
4 Pedro Alpis(pedro.alpis@aluno.cefet-rj.br)
5 Esther Pacitti(Esther.Pacitti@inria.fr)
6 Fabio Porto (fporto@lncc.br)
7 Rafaelli Coutinho(rafaelli.coutinho@cefet-rj.br)
8 Eduardo Ogasawara( eogasawara@ieee.org)

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Reference
# Reference
1 A. Gensler and B. Sick. Performing event detection in time series with swiftevent: an algorithmwith supervised learning of detection criteria. Pattern Analysis and Applications, 21(2):543– 562, 2018.
2 Valery Guralnik and Jaideep Srivastava. Event Detection from Time Series Data. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’99, pages 33–42, New York, NY, USA, 1999. ACM. ISBN 978-1-58113-143-7.
3 R.A. Ariyaluran Habeeb, F. Nasaruddin, A. Gani, I.A. Targio Hashem, E. Ahmed, and M. Imran. Real-time big data processing for anomaly detection: A Survey. International Journal of Information Management, 45:289–307, 2019.
4 B. R. Hiraman, M. C. Viresh, and C. K. Abhijeet. A study of apache kafka in big data streamprocessing. In 2018 International Conference on Information, Communication, Engineering and Technology, ICICET 2018, 2018.
5 Eduardo Ogasawara, Rebecca Salles, Luciana Escobar, Lais Baroni, Janio Lima, and Fabio Porto. Online event detection for sensor data. In Proceedings of the Ibero-Latin-American Congress on Computational Methods in Engineering, 2021.
6 H. Ren, B. Xu, Y. Wang, C. Yi, C. Huang, X. Kou, T. Xing, M. Yang, J. Tong, and Q. Zhang. Time-series anomaly detection service at Microsoft. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 3009–3017, 2019.
7 Rebecca Salles, Luciana Escobar, Lais Baroni, Roccio Zorrilla, Artur Ziviani, Vincius Kreischer, Flavia Delicato, Paulo F. Pires, Luciano Maia, Rafaelli Coutinho, Laura Assis, and Eduardo Ogasawara. Harbinger: Um framework para integração e análise de métodos de detecção de eventos em séries temporais. In Anais do Simpósio Brasileiro de Banco de Dados (SBBD), pages 73–84. SBC, September 2020.
8 P.D. Talagala, R.J. Hyndman, K. Smith-Miles, S. Kandanaarachchi, and M.A. Muñoz. Anomaly Detection in Streaming Nonstationary Temporal Data. Journal of Computational and Graphical Statistics, 29(1):13–27, 2020.
9 C. Truong, L. Oudre, and N. Vayatis. Selective review of offline change point detection methods. Signal Processing, 167, 2020.