Kipf A, Vorona D, Mueller J, Kipf T, Radke B, Leis V, Boncz P, Neumann T, Kemper A (2019)
Publication Type: Conference contribution
Publication year: 2019
Publisher: Association for Computing Machinery
Pages Range: 1937-1940
Conference Proceedings Title: Proceedings of the ACM SIGMOD International Conference on Management of Data
ISBN: 9781450356435
We introduce Deep Sketches, which are compact models of databases that allow us to estimate the result sizes of SQL queries. Deep Sketches are powered by a new deep learning approach to cardinality estimation that can capture correlations between columns, even across tables. Our demonstration allows users to define such sketches on the TPC-H and IMDb datasets, monitor the training process, and run ad-hoc queries against trained sketches. We also estimate query cardinalities with HyPer and PostgreSQL to visualize the gains over traditional cardinality estimators.
APA:
Kipf, A., Vorona, D., Mueller, J., Kipf, T., Radke, B., Leis, V.,... Kemper, A. (2019). Estimating cardinalities with deep sketches. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 1937-1940). Amsterdam, NL: Association for Computing Machinery.
MLA:
Kipf, Andreas, et al. "Estimating cardinalities with deep sketches." Proceedings of the 2019 International Conference on Management of Data, SIGMOD 2019, Amsterdam Association for Computing Machinery, 2019. 1937-1940.
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