Name
Abstract | ||
|---|---|---|
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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3299869.3320218 | Proceedings of the 2019 International Conference on Management of Data |
Keywords | Field | DocType |
cardinality estimation, ml for databases | SQL,Data mining,Computer science,Cardinality,Artificial intelligence,Deep learning,Estimator | Journal |
Volume | ISSN | ISBN |
abs/1904.08223 | 0730-8078 | 978-1-4503-5643-5 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Kipf | 1 | 32 | 11.03 |
| Dimitri Vorona | 2 | 9 | 3.23 |
| Jonas Müller | 3 | 1 | 0.35 |
| Thomas N. Kipf | 4 | 796 | 20.67 |
| Bernhard Radke | 5 | 14 | 1.86 |
| Viktor Leis | 6 | 425 | 30.26 |
| Peter Boncz | 7 | 2517 | 244.81 |
| Thomas Neumann | 8 | 2523 | 156.50 |
| Alfons Kemper | 9 | 3519 | 769.50 |