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English Information

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Authors
# Name
1 Sérgio Lifschitz(sergio@inf.puc-rio.br)
2 Victor Souza(vlins@inf.puc-rio.br)

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Reference
# Reference
1 Aken, D. V., Pavlo, A., Gordon, G. J., and Zhang, B. (2017). Automatic database manage- ment system tuning through large-scale machine learning. In Proceedings of the 2017 ACM International Conference on Management of Data, pages 1009–1024. ACM
2 Ding, B., Das, S., Marcus, R., Wu, W., Chaudhuri, S., and Narasayya, V. R. (2019). AI meets AI: Leveraging query executions to improve index recommendations. In Procs Intl Conf on Management of Data, pages 1241–1258, Amsterdam Netherlands.
3 Kossmann, J., Halfpap, S., Jankrift, M., and Schlosser, R. (2020). Magic mirror in my hand, which is the best in the land? an experimental evaluation of index selection algorithms. Proc. VLDB Endow., 13(12):2382–2395.
4 Oliveira, R. (2015). Ontology-based fine tuning: the case of materialized views (in por- tuguese). Master’s thesis, PUC-Rio.
5 Oliveira, R. (2019). Automatic Selection and Combination of Tuning Actions (in por- tuguese). Phd, PUC-Rio.
6 Perciliano, L., dos V. Santos, Bai ̃ao, F., Haeusler, E. H., Lifschitz, S., and Almeida, A. C. (2021). Inferencing relational database tuning actions with ondbtuning ontology. In Anais do XXXVI Simp. Bras. de Bancos de Dados (SBBD), pages 157–168.
7 Schlosser, R. and Halfpap, S. (2020). A decomposition approach for risk-averse index selection. In 32nd Intl Conf Scientific and Statistical Database Management.
8 Souza, V. (2022). TuningChef: an approach for choosing best cost-benefit tuning actions (in portuguese). Msc, PUC-Rio.
9 Trummer, I. (2022). DB-BERT: A database tuning tool that reads the manual. In Procs Intl Conf on Management of Data, pages 190–203
10 Zhang, J., Zhou, K., Li, G., Liu, Y., Xie, M., Cheng, B., and Xing, J. (2021). CDBTune+: An efficient deep reinforcement learning-based automatic cloud database tuning sys- tem. The VLDB Journal, 30(6):959–987.