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English Information

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Authors
# Name
1 Luciana Perciliano(lperciliano@inf.puc-rio.br)
2 Veronica dos Santos(vdsantos@inf.puc-rio.br)
3 Fernanda Baião(fbaiao@puc-rio.br)
4 Edward Haeusler(hermann@inf.puc-rio.br)
5 Sérgio Lifschitz(sergio@inf.puc-rio.br)
6 Ana Carolina Almeida(ana.almeida@ime.uerj.br)

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Reference
# Reference
1 Aken, D. V., Yang, D., Brillard, S., Fiorino, A., Zhang, B., Billian, C., and Pavlo, A. (2021). An inquiry into machine learning-based automatic configuration tuning services on real-world database management systems. PVLDB, pages 1241–1253.
2 Almeida, A. C., Baião, F. A., 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, page 113538.
3 Almeida, A. C., Campos, M. L. M., Baião, F. A., Lifschitz, S., de Oliveira, R. P., and Schwabe, D. (2019). An ontological perspective for database tuning heuristics. In Int. Conf. on Conceptual Modeling (ER), pages 240–254. Springer.
4 Bassiliades, N. (2020). A tool for transforming semantic web rule language to SPARQL infererecing notation. Intl. Journal Semantic Web Information Systems, pages 87–115.
5 de Almeida, A. C. B. (2013). Framework para apoiar a sintonia fina de banco de dados (in portuguese). PhD thesis, PUC-Rio.
6 Doulaverakis, C., Koutkias, V., Antoniou, G., and Kompatsiaris, I. (2016). Applying sparql-based inference and ontologies for modelling and execution of clinical practice guidelines: a case study on hypertension management. In Knowledge Representation for Health Care, pages 90–107. Springer.
7 Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, pages 199–220.
8 Morelli, E. T., Almeida, A. C., Lifschitz, S., Monteiro, J. M., and Machado, J. C. (2012). Autonomous re-indexing. In Symp. on Applied Computing, pages 893–897. ACM.
9 Promkot, A.-n., Arch-int, S., and Arch-int, N. (2019). The personalized traditional medicine recommendation system using ontology and rule inference approach. In 4th Intl. Conf. on Computer and Communication Systems, pages 96–104. IEEE.
10 Shasha, D. E. and Bonnet, P. (2002). Database Tuning - Principles, Experiments, and Troubleshooting Techniques. Elsevier.
11 Staab, S. and Studer, R. (2010). Handbook on ontologies. Springer Sci & Bus. Media.
12 Suganya, G. and Porkodi, R. (2018). Ontology based information extraction-a review. In Intl. Conf. on Current Trends towards Converging Technologies, pages 1–7. IEEE
13 Valentin, G., Zuliani, M., Zilio, D. C., Lohman, G., and Skelley, A. (2000). Db2 advisor: an optimizer smart enough to recommend its own indexes. In 16th Intl. Conf. on Data Engineering, pages 101–110. IEEE Computer Society.
14 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 system. VLDB, pages 1–29.