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In recent years, machine learning-based cardinality estimation methods arereplacing traditional methods. This change is expected to contribute to one ofthe most important applications of cardinality estimation, the query optimizer,to speed up query processing. However, none of the existing methods do notprecisely estimate cardinalities when relational schemas consist of many tableswith strong correlations between tables/attributes. This paper describes thatmultiple density estimators can be combined to effectively target thecardinality estimation of data with large and complex schemas having strongcorrelations. We propose Scardina, a new join cardinality estimation methodusing multiple partitioned models based on the schema structure.

Scardina: Scalable Join Cardinality Estimation by Multiple Density Estimators