S. Acharya, P. B. Gibbons, V. Poosala, and S. Ramaswamy, Join synopses for approximate query answering, ACM SIGMOD Rec, vol.28, pp.275-286, 1999.

M. Armbrust, Spark SQL: relational data processing in spark, Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp.1383-1394, 2015.

N. Bruno, S. Chaudhuri, and L. Gravano, STHoles: a multidimensional workloadaware histogram, ACM SIGMOD Rec, vol.30, pp.211-222, 2001.

S. Chaudhuri, R. Motwani, and V. Narasayya, On random sampling over joins, ACM SIGMOD Rec, vol.28, pp.263-274, 1999.

C. M. Chen and N. Roussopoulos, Adaptive selectivity estimation using query feedback, vol.23, 1994.

Y. Chen and K. Yi, Two-level sampling for join size estimation, Proceedings of the 2017 ACM International Conference on Management of Data, pp.759-774, 2017.

C. Chow and C. Liu, Approximating discrete probability distributions with dependence trees, IEEE Trans. Inf. Theory, vol.14, issue.3, pp.462-467, 1968.

G. F. Cooper, The computational complexity of probabilistic inference using Bayesian belief networks, Artif. Intell, vol.42, issue.2-3, pp.393-405, 1990.

R. G. Cowell, P. Dawid, S. L. Lauritzen, and D. J. Spiegelhalter, Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks, 2006.

L. Getoor, B. Taskar, and D. Koller, Selectivity estimation using probabilistic models, ACM SIGMOD Rec, vol.30, pp.461-472, 2001.

D. Heckerman, D. Geiger, and D. M. Chickering, Learning Bayesian networks: the combination of knowledge and statistical data, Mach. Learn, vol.20, issue.3, pp.197-243, 1995.

M. Heimel, M. Kiefer, and V. Markl, Self-tuning, GPU-accelerated kernel density models for multidimensional selectivity estimation, Proceedings of the, 2015.

, ACM SIGMOD International Conference on Management of Data, pp.1477-1492, 2015.

J. M. Hellerstein, Looking back at postgres, 2019.

F. K. Hwang, D. S. Richards, and P. Winter, The Steiner Tree Problem, vol.53, 1992.

Y. E. Ioannidis and S. Christodoulakis, On the propagation of errors in the size of join results, vol.20, 1991.

Y. E. Ioannidis and S. Christodoulakis, Optimal histograms for limiting worst-case error propagation in the size of join results, ACM Trans. Database Syst. (TODS), vol.18, issue.4, pp.709-748, 1993.

T. Jaakkola, D. Sontag, A. Globerson, and M. Meila, Learning Bayesian network structure using LP relaxations, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp.358-365, 2010.

F. V. Jensen, An Introduction to Bayesian Networks, vol.210, 1996.

A. Kipf, T. Kipf, B. Radke, V. Leis, P. Boncz et al., Learned cardinalities: estimating correlated joins with deep learning, 2018.

R. P. Kooi, The optimization of queries in relational databases, 1980.

V. Leis, A. Gubichev, A. Mirchev, P. Boncz, A. Kemper et al., How good are query optimizers, really?, Proc. VLDB Endowment, vol.9, pp.204-215, 2015.

V. Leis, Query optimization through the looking glass, and what we found running the join order benchmark, J, vol.27, pp.1-26, 2018.

V. Leis, B. Radke, A. Gubichev, A. Kemper, and T. Neumann, Cardinality estimation done right: index-based join sampling, 2017.

F. Li, B. Wu, K. Yi, and Z. Zhao, Wander join: online aggregation via random walks, Proceedings of the 2016 International Conference on Management of Data, pp.615-629, 2016.

R. J. Lipton and J. F. Naughton, Query size estimation by adaptive sampling, Proceedings of the Ninth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp.40-46, 1990.

G. Moerkotte, T. Neumann, and G. Steidl, Preventing bad plans by bounding the impact of cardinality estimation errors, Proc. VLDB Endowment, vol.2, issue.1, pp.982-993, 2009.

M. Muralikrishna and D. J. Dewitt, Equi-depth multidimensional histograms. SIG-MOD Rec, vol.17, pp.28-36, 1988.

S. Muthukrishnan, V. Poosala, and T. Suel, On rectangular partitionings in two dimensions: algorithms, complexity and applications, ICDT 1999, vol.1540, pp.236-256, 1999.

F. Olken, Random sampling from databases, 1993.

G. Piatetsky-shapiro and C. Connell, Accurate estimation of the number of tuples satisfying a condition, ACM SIGMOD Rec, vol.14, issue.2, pp.256-276, 1984.

M. Poess, R. O. Nambiar, and D. Walrath, Why you should run TPC-DS: a workload analysis, Proceedings of the 33rd International Conference on Very Large Databases, pp.1138-1149, 2007.

V. Poosala, P. J. Haas, Y. E. Ioannidis, and E. J. Shekita, Improved histograms for selectivity estimation of range predicates, ACM Sigmod Rec, vol.25, pp.294-305, 1996.

N. Robertson and P. D. Seymour, Graph minors. II: algorithmic aspects of tree-width, J. Algorithms, vol.7, issue.3, pp.309-322, 1986.

P. G. Selinger, M. M. Astrahan, D. D. Chamberlin, R. A. Lorie, and T. G. Price, Access path selection in a relational database management system, Proceedings of the 1979 ACM SIGMOD International Conference on Management of Data, pp.23-34, 1979.

M. Stillger, G. M. Lohman, V. Markl, and M. Kandil, Leo-db2's learning optimizer, vol.1, pp.19-28, 2001.

M. Traverso, Presto: interacting with petabytes of data at Facebook, 2013.

K. Tzoumas, A. Deshpande, and C. S. Jensen, Lightweight graphical models for selectivity estimation without independence assumptions, Proc. VLDB Endowment, vol.4, pp.852-863, 2011.

D. Vengerov, A. C. Menck, M. Zait, and S. P. Chakkappen, Join size estimation subject to filter conditions, Proc. VLDB Endowment, vol.8, pp.1530-1541, 2015.