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English Information

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Authors
# Name
1 Alexandre Tolomeotti Enokida(atenokida@gmail.com)
2 Eduardo Henrique Monteiro Pena(eduardopena@utfpr.edu.br)

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Reference
# Reference
1 Alon, N., Gibbons, P. B., Matias, Y., e Szegedy, M. (1999). Tracking join and self-join sizes in limited storage. In Proceedings of the Eighteenth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pages 10–20. Association for Computing Machinery.
2 Cormode, G., Garofalakis, M., Haas, P. J., e Jermaine, C. (2012). Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches. Found. Trends Databases, 4(1–3):1–294.
3 Cormode, G. e Muthukrishnan, S. (2005). An improved data stream summary: the count-min sketch and its applications. Journal of Algorithms, 55(1):58–75.
4 Flajolet, P., Fusy, É., Gandouet, O., e Meunier, F. (2007). Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm. Discrete mathematics & theoretical computer science, (Proceedings).
5 Freitag, M. e Neumann, T. (2019). Every row counts: Combining sketches and sampling for accurate group-by result estimates. In Proceedings of the 9th Conference on Innovative Data Systems Research, CIDR 2019. www.cidrdb.org.
6 Ioannidis, Y. (2003). The history of histograms (abridged). In Proceedings of the 29th International Conference on Very Large Data Bases - Volume 29, pages 19–30. VLDB Endowment.
7 Lee, K., Dutt, A., Narasayya, V., e Chaudhuri, S. (2023). Analyzing the impact of cardinality estimation on execution plans in Microsoft SQL Server. Proc. VLDB Endow., 16(11):2871–2883.
8 Leis, V., Gubichev, A., Mirchev, A., Boncz, P., Kemper, A., e Neumann, T. (2015). How good are query optimizers, really? Proc. VLDB Endow., 9(3):204–215.
9 Leis, V., Radke, B., Gubichev, A., Mirchev, A., Boncz, P., Kemper, A., e Neumann, T. (2018). Query optimization through the looking glass, and what we found running the Join Order Benchmark. The VLDB Journal, 27(5):643–668.
10 Liu, Z., Deep, S., Fariha, A., Psallidas, F., Tiwari, A., e Floratou, A. (2024). Rapidash: Efficient detection of constraint violations. Proc. VLDB Endow., 17(8):2009–2021.
11 Moerkotte, G. (2024). Cardinality estimation for having-clauses. Proc. VLDB Endow., 18(1):28–41.
12 Moerkotte, G. (2025). Building query compilers. University of Mannheim. https://pi3.informatik.uni-mannheim.de/~moer/querycompiler.pdf.
13 Moerkotte, G., Neumann, T., e Steidl, G. (2009). Preventing bad plans by bounding the impact of cardinality estimation errors. Proc. VLDB Endow., 2(1):982–993.
14 Pena, E. H. M., de Almeida, E. C., e Naumann, F. (2021). Fast detection of denial constraint violations. Proc. VLDB Endow., 15(4):859–871.
15 Selinger, P. G., Astrahan, M. M., Chamberlin, D. D., Lorie, R. A., e Price, T. G. (1979). Access path selection in a relational database management system. In Proceedings of the 1979 ACM SIGMOD International Conference on Management of Data, pages 23–34. Association for Computing Machinery.
16 Silberschatz, A., Korth, H. F., e Sudarshan, S. (2020). Database system concepts, seventh edition. McGraw-Hill Book Company.
17 Wang, F., Chen, Q., Li, Y., Yang, T., Tu, Y., Yu, L., e Cui, B. (2023). JoinSketch: A sketch algorithm for accurate and unbiased inner-product estimation. Proc. ACM Manag. Data, 1(1):81.