Towards a cloud-based framework for online and integrated event detection

• Janio Lima Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)
• Rebecca Salles Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ) http://orcid.org/0000-0002-1001-3839
• Luciana Escobar Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)
• Cristiane Géa Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ) / Federal Fluminense University (UFF) http://orcid.org/0000-0001-8133-5774
• Pedro Alpis Fernandes Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)
• Esther Pacitti INRIA / University of Montpellier
• Fabio Porto National Laboratory for Scientific Computing
• Rafaelli Coutinho Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)
• Eduardo Ogasawara Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)

DOI: https://doi.org/10.5753/sbbd_estendido.2022.21865

Abstract

Time series events detection relates to the study of techniques for detecting points in a series with special meaning which differs from the expected behavior of the dataset. In scenarios such as digital twins and IoT devices, there is natural generation and traffic of data in the cloud. Event detection is critical for timely decision-making. Since many methods for detecting events target different types selecting a suitable method makes the task more difficult. In this context, this article proposes a cloud-based framework called Harbinger Nimbus. The implementation was evaluated on the Microsoft Azure platform.

Keywords: cloud computing, event detection, online event

References

Gensler, A. and Sick, B. (2018). Performing event detection in time series with swiftevent: an algorithm with supervised learning of detection criteria. Pattern Analysis and Applications, 21(2):543–562.

Guralnik, V. and Srivastava, J. (1999). 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. ACM.

Habeeb, R. A., Nasaruddin, F., Gani, A., Targio Hashem, I., Ahmed, E., and Imran, M. (2019). Real-time big data processing for anomaly detection: A Survey. International Journal of Information Management, 45:289–307.

Hiraman, B. R., Viresh, M. C., and Abhijeet, C. K. (2018). A study of apache kafka in big data stream processing. In 2018 International Conference on Information, Communication, Engineering and Technology, ICICET 2018.

Ogasawara, E., Salles, R., Escobar, L., Baroni, L., Lima, J., and Porto, F. (2021). Online event detection for sensor data. In Proceedings of the Ibero-Latin-American Congress on Computational Methods in Engineering.

Ren, H., Xu, B., Wang, Y., Yi, C., Huang, C., Kou, X., Xing, T., Yang, M., Tong, J., and Zhang, Q. (2019). Time-series anomaly detection service at Microsoft. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 3009– 3017.

Salles, R., Escobar, L., Baroni, L., Zorrilla, R., Ziviani, A., Kreischer, V., Delicato, F., Pires, P. F., Maia, L., Coutinho, R., Assis, L., and Ogasawara, E. (2020). 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.

Talagala, P., Hyndman, R., Smith-Miles, K., Kandanaarachchi, S., and Muñoz, M. (2020). Anomaly Detection in Streaming Nonstationary Temporal Data. Journal of Computational and Graphical Statistics, 29(1):13–27.

Truong, C., Oudre, L., and Vayatis, N. (2020). Selective review of offline change point detection methods. Signal Processing, 167.