  In recent years, machine learning-based cardinality estimation methods are
replacing traditional methods. This change is expected to contribute to one of
the most important applications of cardinality estimation, the query optimizer,
to speed up query processing. However, none of the existing methods do not
precisely estimate cardinalities when relational schemas consist of many tables
with strong correlations between tables/attributes. This paper describes that
multiple density estimators can be combined to effectively target the
cardinality estimation of data with large and complex schemas having strong
correlations. We propose Scardina, a new join cardinality estimation method
using multiple partitioned models based on the schema structure.
