SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Gabriel Fornitano(d2021009763@unifei.edu.br)
2 Flávio Mota(flavio.belizario.mota@gmail.com)
3 Vanessa Souza(vanessasouza@unifei.edu.br)
4 Arcilan Assireu(arcilan@unifei.edu.br)
5 Melise Veiga de Paula(melise@unifei.edu.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Assireu, A. T., Fisch, G., Carvalho, V. S. O., Pimenta, F. M., de Freitas, R. M., Saavedra, O. R., Neto, F. L. A., J´unior, A. R. T., Oliveira, D. Q., Lopes, D. C. P., de Lima, S. L., Marcondes, L. G. P., and Rodrigues, W. K. S. (2024). Sea breeze-driven effects on wind down-ramps: Their implications for wind farms along the north-east coast of brazil. Energy, 294:130804.
2 Boettiger, C. (2015). An introduction to docker for reproducible research. ACM SIGOPS Operating Systems Review, 49(1):71–79.
3 Box, G. E. P., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control. Wiley, 5 edition.
4 Chastre, C. and L´ucio, V. (2012). Torres pr´e-fabricadas de bet˜ao para suporte de turbinas e´olicas. In Estruturas pr´e-moldadas no mundo – Aplicac¸ ˜oes e comportamento estru- tural, pages 91–106. Universidade NOVA de Lisboa.
5 Cielen, D., Meysman, A. D. B., and Ali, M. (2021). Data Science: Principles and Prac- tice. Manning Publications.
6 Elsaraiti, M. and Merabet, A. (2021). A comparative analysis of the arima and lstm predic- tive models and their effectiveness for predicting wind speed. Energies, 14(20):6782.
7 Epstein, B. and Roberts, P. (2022). Accelerate Machine Learning with a Unified Analytics Architecture. O’Reilly Media, Inc., Sebastopol, CA, USA.
8 Fard, A., Zhang, B., Katepalli, K., Stonebraker, M., and Rundensteiner, E. A. (2020). Vertica-ml: Distributed machine learning in vertica database. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pages 755– 768. ACM.
9 Grigonyt˙e, E. and Butkeviˇci¯ut˙e, E. (2016). Short-term wind speed forecasting using arima model. Energetika, 62(1–2):17–26.
10 Hyndman, R. J. and Athanasopoulos, G. (2021). Forecasting: Principles and Practice. OTexts, Melbourne, Australia, 3 edition. Accessed on March 26, 2025.
11 Lamb, A., Fuller, M., Varadarajan, R., Tran, N., Vandiver, B., Doshi, L., and Bear, C. (2012). The vertica analytic database: C-store 7 years later. Vertica Systems, An HP Company.
12 Liu, X., Lin, Z., and Feng, Z. (2021). Short-term offshore wind speed forecast by seasonal arima-a comparison against gru and lstm. Energy, 227:120492.
13 Lustosa, H., Costa, F., Guimar˜aes, J., and de Oliveira, D. (2020). Savime: An array dbms for simulation analysis and ml models predictions. In International Conference on Database and Expert Systems Applications, pages 357–367. Springer.
14 Raschka, S., Patterson, J., and Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11(4):193.
15 Salman, A. G. and Kanigoro, B. (2021). Visibility forecasting using autoregressive inte- grated moving average (arima) models. Procedia Computer Science, 181:586–593.
16 ertica (2025). Arima - vertica 25.1.x documentation. Accessed: March 26, 2025.