Autor:	Kipf, Andreas, Vorona, Dimitri, Müller, Jonas, Kipf, Thomas, Radke, Bernhard, Leis, Viktor, Boncz, Peter, Neumann, Thomas, Kemper, Alfons
Rok vydání:	2019
Předmět:	Computer Science - Databases
Druh dokumentu:	Working Paper
Popis:	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. Comment: To appear in SIGMOD'19
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1904.08223 Zobrazit plný text záznamu View this record from Arxiv