Cardinality estimation using artificial neural networks

In an approach for generating a selectivity estimation, one or more processors generate an artificial neural network and receive a DBMS query comprising one or more predicates. One or more processors replace one or more predicates in the one or more predicates that have strict operators with one or more predicates that have non-strict operators. One or more processors generate a selectivity function from the one or more predicates that has one or more arguments that are each comprised of an upper bound and a lower bound for a value in a predicate. One or more processors generate a training data set from a data distribution in the database and train the artificial neural network on the training data set to compute the selectivity function. One or more processors generate a selectivity estimation with the artificial neural network for one or more predicates in the DBMS query.