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English Information

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Authors
# Name
1 Eric Leão(eleao@inf.puc-rio.br)
2 Sergio Lifschitz(sergio@inf.puc-rio.br)
3 Ana Carolina Almeida(ana.almeida@ime.uerj.br)
4 Edward Haeusler(hermann@inf.puc-rio.br)

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Reference
# Reference
1 Alencar, N., Brayner, A., Monteiro, J. M., and de Aguiar Moraes Filho, J. (2019). Dac-join: A join operator for improving database performance on modern hardware. Concurr. Comput. Pract. Exp., 31(17)
2 Almeida, A. C., Baião, F., Lifschitz, S., Schwabe, D., and Campos, M. L. M. (2021). Tun-Ocm: A model-driven approach to support database tuning decision making. Decision Support Systems, 145:113538
3 Almeida, A. C., Campos, M. L. M., Baião, F., Lifschitz, S., de Oliveira, R. P., and Schwabe, D. (2019). An ontological perspective for database tuning heuristics. In Laender, A. H. F., Pernici, B., Lim, E.-P., and de Oliveira, J. P. M., editors, Conceptual Modeling, pages 240–254, Cham. Springer International Publishing
4 Calero, C., Ruiz, F., Baroni, A., e Abreu, F. B., and Piattini, M. (2006). An ontological approach to describe the sql: 2003 object-relational features. Computer Standards & Interfaces, 28(6):695–713.
5 de Aguiar, C. Z., Falbo, R. A., and Souza, V. E. S. (2018). Ontological Representation of Relational Databases. In Proc. of the 11th Seminar on Ontology Research in Brazil (ONTOBRAS 2018), pages 140–151, Sao Paulo, SP, Brazil. CEUR.
6 de Araújo, A. H. M., Monteiro, J. M., de Macêdo, J. A. F., Tavares, J. A., Brayner, A., and Lifschitz, S. (2014). On using an online, automatic and non-intrusive approach for rewriting SQL queries. J. Inf. Data Manag., 5(1):28–39.
7 Dou, H., Jin, L., Zhou, Y., He, J., Zhang, Y., and Zheng, Z. (2026). Demotuner: Automatic performance tuning for database management systems based on demonstration reinforcement learning.
8 Fuentes, A. D., Almeida, A. C., de Carvalho Costa, R. L., Braganholo, V., and Lifschitz, S. (2018). Database tuning with partial indexes. In Lóscio, B. F., Dorneles, C. F., and Barioni, M. C. N., editors, XXXIII Simpósio Brasileiro de Banco de Dados, SBBD 2018, Rio de Janeiro, RJ, Brazil, August 25-26, 2018, pages 181–192. SBC.
9 Giannakouris, V. and Trummer, I. (2025). λ-tune: Harnessing large language models for automated database system tuning. Proc. ACM Manag. Data, 3(1).
10 Lao, J., Wang, Y., Li, Y., Wang, J., Zhang, Y., Cheng, Z., Chen, W., Tang, M., and Wang, J. (2025). Gptuner: An llm-based database tuning system. ACM SIGMOD Record, 54(1):101–110
11 Li, Y., Li, H., Pu, Z., Zhang, J., Zhang, X., Ji, T., Sun, L., Li, C., and Chen, H. (2024). Is large language model good at database knob tuning? a comprehensive experimental evaluation. arXiv preprint arXiv:2408.02213
12 Li, Y., Li, H., Zhang, J., Borovica-Gajic, R., Wang, S., Zhang, T., Chen, J., Shi, R., Li, C., and Chen, H. (2025). Agenttune: An agent-based large language model framework for database knob tuning. Proc. ACM Manag. Data, 3(6):1–29
13 Liu, F., Wei, R., Wang, H., Ding, Z., Mo, Z., Zhou, K., and Liu, Y. (2026). Arbiter: Towards joint and fine-grained index and partition tuning in analytical databases. Inf. Process. Manag., 63(5)
14 Metamodel, C. W. (2003). Common warehouse metamodel (cwm) specification. Inc., Needham, MA, USA
15 Mozaffari, M., Dignos, A., Gamper, J., and Störl, U. (2024). Self-tuning database systems: A systematic literature review of automatic database schema design and tuning. ACM Computing Surveys, 56(11).
16 Oliveira, R. P., Baião, F., Machado, J., Almeida, A. C., and Lifschitz, S. (2022). Autonomic combination and selection of tuning actions. In Anais do XXXVII Simpósio Brasileiro de Bancos de Dados (SBBD), pages 39–51.
17 Ouared, A., Ouhammou, Y., and Roukh, A. (2016). A meta-advisor repository for database physical design. In International Conference on Model and Data Engineering, pages 72–87. Springer
18 Perciliano, L. S. S., dos Santos, V., Baião, F., Haeusler, E. H., Lifschitz, S., and Almeida, A. C. (2021). Inferencing relational database tuning actions with ondbtuning ontology. In Anais do XXXVI Simpósio Brasileiro de Bancos de Dados (SBBD), pages 157–168.
19 Souza, V. A. L. L. and Lifschitz, S. (2022). Tuningchef: an approach for choosing the best cost-benefit database tuning actions. In Anais do XXXVII Simpósio Brasileiro de Bancos de Dados (SBBD).
20 Wang, X., Wu, W., Narasayya, V., and Chaudhuri, S. (2026). Evaluating the practical effectiveness of llm-driven index tuning with microsoft database tuning advisor, https://doi.org/10.48550/arXiv.2603.09181.
21 Wu, Y., Zhou, X., Zhang, Y., and Li, G. (2024). Automatic index tuning: A survey. IEEE Transactions on Knowledge and Data Engineering, 36(12):7657–7676
22 Xue, S., Jiang, C., Shi, W., Cheng, F., Chen, K., Yang, H., Zhang, Z., He, J., Zhang, H., Wei, G., Zhao, W., Zhou, F., Qi, D., Yi, H., and Liu, S. (2024). Db-gpt: Large language model meets database. Data Science and Engineering, 9:1–16
23 Zhao, X., Zhou, X., and Li, G. (2023). Automatic database knob tuning: A survey. IEEE Transactions on Knowledge and Data Engineering, 35(12):12470–12490.