Automated query predicate selectivity prediction using machine learning models

A computer system for cardinality estimation, comprising:

• one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more computer-readable tangible storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising:
• accessing database relations;
• collecting a random sample from each of the database relations;
• generate training data from the random sample of each of the database relations;
• creating a cumulative frequency function (CFF) model from the generated training data;
• and
• predicting a cardinality estimation of a plurality of structured query language (SQL) predicates.

A computer program product for cardinality estimation, comprising:

• one or more computer-readable storage media and program instructions stored on at least one of the one or more computer-readable storage media, the program instructions executable by a processor to cause the processor to perform a method comprising:
• accessing database relations;
• collecting a random sample from each of the database relations;
• generate training data from the random sample of each of the database relations;
• creating a cumulative frequency function (CFF) model from the generated training data;
• and
• predicting a cardinality estimation of a plurality of structured query language (SQL) predicates.