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 Gustavo Valença(gustavo.valenca@lsbd.ufc.br)
2 Francisco Pereira(lucas.falcao@lsbd.ufc.br)
3 Felipe Brito(felipe.timbo@lsbd.ufc.br)
4 Victor de Farias(victor.farias@lsbd.ufc.br)
5 Javam Machado(javam.machado@lsbd.ufc.br)

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

Reference
# Reference
1 Che, Z., Purushotham, S., Cho, K., Sontag, D., and Liu, Y. (2018). Recurrent neural networks for multivariate time series with missing values. Scientific reports, 8(1):6085.
2 Chen, L., Zhu, Z., Li, A., Mashhadi, N., Frickey, R., Ye, J., and Guo, X. (2022). Ssd drive failure prediction on alibaba data center using machine learning. In 2022 IEEE International Memory Workshop (IMW), pages 1–4. IEEE.
3 dos Santos Lima, F. D., Amaral, G. M. R., de Moura Leite, L. G., Gomes, J. P. P., and de Castro Machado, J. (2017). Predicting failures in hard drives with lstm networks. In 2017 Brazilian Conference on Intelligent Systems (BRACIS), pages 222–227. IEEE.
4 Felix, G. L., Pereira, F. L., Praciano, F. D., Gomes, J. P., and Machado, J. C. (2023). Feature selection for remaining useful life prediction in hard disk drives with missing data. In Anais Estendidos do XXXVIII Simposio Brasileiro de Bancos de Dados, pages 57–63. SBC.
5 Han, S., Lee, P. P., Xu, F., Liu, Y., He, C., and Liu, J. (2021). An in-depth study of correlated failures in production ssd-based data centers. In 19th USENIX Conference on File and Storage Technologies (FAST 21). USENIX Association.
6 Lima, F. D. S., Pereira, F. L. F., Chaves, I. C., Gomes, J. P. P., and Machado, J. C. (2018). Evaluation of recurrent neural networks for hard disk drives failure prediction. In 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), pages 85–90. IEEE.
7 Lima, F. D. S., Pereira, F. L. F., Chaves, I. C., Machado, J. C., and Gomes, J. P. P. (2021). Predicting the health degree of hard disk drives with asymmetric and ordinal deep neural models. IEEE Transactions on Computers, 70(2):188–198.
8 Lu, R., Xu, E., Zhang, Y., Zhu, Z., Wang, M., Zhu, Z., Xue, G., Li, M., and Wu, J. (2022). NVMe SSD failures in the field: the Fail-Stop and the Fail-Slow. In 2022 USENIX Annual Technical Conference (USENIX ATC 22), pages 1005–1020, Carlsbad, CA. USENIX Association.
9 Maneas, S., Mahdaviani, K., Emami, T., and Schroeder, B. (2020). A study of {SSD} reliability in large scale enterprise storage deployments. In 18th USENIX Conference on File and Storage Technologies (FAST 20), pages 137–149.
10 Murray, J. F., Hughes, G. F., Kreutz-Delgado, K., and Schuurmans, D. (2005). Machine learning methods for predicting failures in hard drives: A multiple-instance application. Journal of Machine Learning Research, 6(5).
11 Ottem, E. and Plummer, J. (1995). Playing it smart: The emergence of reliability prediction technology. Technical report, Technical report, Seagate Technology Paper.
12 Pereira, F. L. F., Bucar, R. C., Brito, F. T., Gomes, J. P. P., and Machado, J. C. (2022). Predicting failures in hdds with deep nn and irregularly-sampled data. In Brazilian Conference on Intelligent Systems, pages 196–209. Springer.
13 Xu, F., Han, S., Lee, P. P., Liu, Y., He, C., and Liu, J. (2021). General feature selection for failure prediction in large-scale ssd deployment. In 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pages 263–270. IEEE.