Automatic feature learning from a relational database for predictive modelling

A system for automatic feature learning for predictive modelling in a computing environment, comprising:

• a hardware processor;
• and
• a hardware memory storing executable instructions, wherein, when executed by the hardware processor, the executable instructions cause the hardware processor to:
• join a first table and a second table based on an edge between the first table and the second table defined by an entity graph, wherein a resulting joined table is connected by a column of data;
• use the resulting joined table as an input into one or more neural network operations that transform the resulting joined table to one or more features to predict a target variable, wherein the one or more neural network operations are defined as one or more relational neural network operations and an embedded neural network operation in an embedded layer associated with the resulting joined table;
• input unstructured data of the resulting joined table into the embedded layer associated with the resulting joined table, wherein unstructured data is selected from a group consisting of spatial-temporal data, time-series data, sequence data, item set data, number set data, singleton data, text data and image data;
• transform the unstructured data into numerical data using the embedded neural network operation in the embedded layer associated with the resulting joined table;
• and
• input the numerical data transformed by the embedded neural network operation into the one or more relational neural network operations that transform the numerical data to one or more features to predict the target variable.

A computer program product for, by a processor, automatic feature learning for predictive modelling in a computing environment, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:

• an executable portion that joins a first table and a second table based on an edge between the first table and the second table defined by an entity graph, wherein a resulting joined table is connected by a column of data;
• an executable portion that uses the resulting joined table as an input into one or more neural network operations that transform the resulting joined table to one or more features to predict a target variable, wherein the one or more neural network operations are defined as one or more relational neural network operations and an embedded neural network operation in an embedded layer associated with the resulting joined table;
• an executable portion that inputs unstructured data of the resulting joined table into the embedded layer associated with the resulting joined table, wherein unstructured data is selected from a group consisting of spatial-temporal data, time-series data, sequence data, item set data, number set data, singleton data, text data and image data;
• an executable portion that transforms the unstructured data into numerical data using the embedded neural network operation in the embedded layer associated with the resulting joined table;
• and
• an executable portion that inputs the numerical data transformed by the embedded neural network operation into the one or more relational neural network operations that transform the numerical data to one or more features to predict the target variable.