1. Agarwal, V., Bhattacharyya, C., Niranjan, T., Susarla, S.: Discovering rules from disk events for predicting hard drive failures. In: Proceedings of the IEEE International Conference on Machine Learning and Applications, vol. 1, pp. 782–786. IEEE, December 2009
2. Botezatu, M.M., Giurgiu, I., Bogojeska, J., Wiesmann, D.: Predicting disk replacement towards reliable data centers. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 13–17-August, pp. 39–48. ACM Press, August 2016
3. Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. vol. 13–17-August, pp. 785–794. ACM Press, August 2016
4. alibaba edu: The dataset of over 200 thousands hard disk drives in alibaba cloud’s data centers (2020). https://github.com/alibaba-edu/dcbrain/tree/master/diskdata
5. Han, S., Lee, P.P., Shen, Z., He, C., Liu, Y., Huang, T.: Toward adaptive disk failure prediction via stream mining. In: Proceedings of the IEEE International Conference on Distributed Computing Systems (2020)