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Learned Cardinalities: Estimating Correlated Joins with Deep Learning
[article]
2018
arXiv
pre-print
We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set convolutional network, tailored to representing relational query plans, that employs set semantics to capture query features and true cardinalities. MSCN builds on sampling-based estimation, addressing its weaknesses when no sampled tuples qualify a predicate, and in capturing join-crossing correlations. Our evaluation of MSCN using a real-world dataset shows that deep learning significantly enhances the
arXiv:1809.00677v2
fatcat:2oqbfpvop5h2pbsovvha5p3xzq