Automatic feature learning from a relational database for predictive modelling

Patent metadata

Field	Value
Publication number	US-11386128-B2
Application number	US-202016947956-A
Country	US
Kind code	B2
Filing date	Aug 25, 2020
Priority date	Sep 25, 2017
Publication date	Jul 12, 2022
Grant date	Jul 12, 2022

Abstract

Embodiments for automatic feature learning for predictive modeling in a computing environment by a processor. A first table and a second table are joined based on an edge between the first table and the second table defined by an entity graph thereby creating a resulting joined table that is connected by a column of data. The resulting joined table is used 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.

First claim

The invention claimed is:

1. A method, by a processor, for automatic feature learning for predictive modelling in a computing environment, comprising:
joining 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;
using 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;
inputting 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;
transforming the unstructured data into numerical data using the embedded neural network operation in the embedded layer associated with the resulting joined table; and
inputting 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.

2. The method of claim 1 , further including inputting the numerical data of the resulting joined table into the one or more relational neural network operations that transform the numerical data to the one or more features to predict the target variable.

3. The method of claim 1 , further comprising inputting the numerical data according to a hierarchical order of the resulting joined table into the one or more relational neural network operations that transform the numerical data to the one or more features to predict the target variable.

4. The method of claim 1 , further comprising collecting, by the device, features extracted from the first table and the second table by traversing the entity graph.

5. The method of claim 4 , wherein the entity graph is traversed to a depth based on a defined criterion.

6. 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.

7. The system of claim 6 , wherein, when executed by the hardware processor, the executable instructions further cause the hardware processor to input the numerical data of the resulting joined table into the one or more relational neural network operations that transform the numerical data to the one or more features to predict the target variable.

8. The system of claim 6 , wherein, when executed by the hardware processor, the executable instructions further cause the hardware processor to input the numerical data according to a hierarchical order of the resulting joined table into the one or more relational neural network operations that transform the numerical data to the one or more features to predict the target variable.

9. The system of claim 6 , wherein, when executed by the hardware processor, the executable instructions further cause the hardware processor to collect features extracted from the first table and the second table by traversing the entity graph.

10. The system of claim 9 , wherein the entity graph is traversed to a depth based on a defined criterion.

11. 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.

12. The computer program product of claim 11 , further including an executable portion that inputs the numerical data of the resulting joined table into the one or more relational neural network operations that transform the numerical data to the one or more features to predict the target variable.

13. The computer program product of claim 11 , further including an executable portion that inputs the numerical data according to a hierarchical order of the resulting joined table into the one or more relational neural network operations that transform the numerical data to the one or more features to predict the target variable.

14. The computer program product of claim 11 , further including an executable portion that collects features extracted from the first table and the second table by traversing the entity graph.

15. The computer program product of claim 14 , wherein t

Assignees

• IBM

Inventors

• Buesser Beat
• Hoang Thanh Lam
• Sinn Mathieu
• Tran Ngoc Minh

Classifications

• G06N3/08 Primary

  Learning methods · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N3/042

  Knowledge-based neural networks; Logical representations of neural networks · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N3/045

  Combinations of networks · CPC title