Automatic feature extraction from a relational database

Patent metadata

Field	Value
Publication number	US-11645311-B2
Application number	US-202117451512-A
Country	US
Kind code	B2
Filing date	Oct 20, 2021
Priority date	Jan 17, 2017
Publication date	May 9, 2023
Grant date	May 9, 2023

Abstract

Techniques facilitating automatic feature extraction from a relational database are provided. In an embodiment, a method can include generating an entity graph based on a relational database, wherein the entity graph comprises a first node associated with a first table in the relational database and a second node associated with a second table in the relational database. In another embodiment, the method can include joining the first table and the second table based on an edge between the first table and the second table defined by the entity graph, wherein a resulting joined table is connected by a column of data. In another embodiment, the method can include extracting a feature from the column of data using a data mining algorithm selected from a set of data mining algorithms based on a type of data in the column of data.

First claim

What is claimed is:

1. A computer-implemented method, comprising:
determining, by a device operatively coupled to a processor, a type of data in a column of data of a joined table of a relational database, wherein the column of data is formed based on merging a first column of data of a first table of the relational database and a second column of data of a second table of the relational database; and
extracting, by the device, one or more features from tables of the relational database based on the column of data of the joined table using a data mining algorithm selected based on the type of data in the column of data.

2. The computer-implemented method of claim 1 , wherein the type of data in the column of 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.

3. The computer-implemented method of claim 1 , further comprising forming, by the device, the joined table based on an edge between a first node of an entity graph representing the first table and a second node of the entity graph representing the second table.

4. The computer-implemented method of claim 1 , further comprising:
selecting, by the device, a feature from the one or more features based on a relevance to a target variable that is defined by an entity in a main table associated with a root node of an entity graph representing the relational database.

5. The computer-implemented method of claim 1 , further comprising:
collecting, by the device, the one or more features extracted from tables of the relational database by traversing an entity graph representing the relational database.

6. The computer-implemented method of claim 5 , wherein the entity graph is traversed to a depth based on a defined criterion related to processing efficiency.

7. The computer-implemented method of claim 5 , wherein the entity graph is traversed to a depth based on a defined criterion related to a user input.

8. A system, comprising:
a memory that stores computer executable components; and
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:
a feature extraction component configured to:
determine a type of data in a column of data of a joined table of a relational database, wherein the column of data is formed based on merging a first column of data of a first table of the relational database and a second column of data of a second table of the relational database; and
extract one or more features from tables of the relational database based on the column of data of the joined table using a data mining algorithm selected based on the type of data in the column of data extract features based on the joined table.

9. The system of claim 8 , wherein the type of data in the column of 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.

10. The system of claim 8 , further comprising:
a joining component configured to form the joined table based on an edge between a first node of an entity graph representing the first table and a second node of the entity graph representing the second table.

11. The system of claim 8 , further comprising:
an identification component configured to select a feature from the one or more features based on a relevance to a target variable that is defined by an entity in a main table associated with a root node of an entity graph representing the relational database.

12. The system of claim 8 , further comprising:
a collection component configured to collect the one or more features extracted from tables of the relational database by traversing an entity graph representing the relational database.

13. The system of claim 12 , wherein the entity graph is traversed to a depth based on a defined criterion related to processing efficiency.

14. The system of claim 12 , wherein the entity graph is traversed to a depth based on a defined criterion related to a user input.

15. A computer program product to provide automatic feature extraction, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing component to cause the processing component to:
determine a type of data in a column of data of a joined table of a relational database, wherein the column of data is formed based on merging a first column of data of a first table of the relational database and a second column of data of a second table of the relational database; and
extract one or more features from tables of the relational database based on the column of data of the joined table using a data mining algorithm selected based on the type of data in the column of data extract features based on the joined table.

16. The computer program product of claim 15 , wherein the type of data in the column of 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.

17. The computer program product of claim 15 , wherein the program instructions are further executable by the processing component to cause the processing component to:
form the joined table based on an edge between a first node of an entity graph representing the first table and a second node of the entity graph representing the second table.

18. The computer program product of claim 15 , wherein the program instructions are further executable by the processing component to cause the processing component to:
select a feature from the one or more features based on a relevance to a target variable that is defined by an entity in a main table associated with a root node of an entity graph representing the relational database.

19. The computer program product of claim 15 , wherein the program instructions are further executable by the processing component to cause the processing component to:
collect the one or more features extracted from tables of the relational database by traversing an entity graph representing the relational database.

20. The computer program product of claim 19 , wherein the entity graph is traversed to a depth based on a defined criterion related to processing efficiency.

Assignees

• IBM

Inventors

• Chen Bei
• Hoang Thanh Lam
• Mai Tiep
• Sinn Mathieu
• Thiebaut Johann-Michael

Classifications

• G06F16/288 Primary

  Entity relationship models · CPC title

• G06F16/9024

  Graphs; Linked lists (G06F16/9027 takes precedence) · CPC title

• G06F16/2465

  Query processing support for facilitating data mining operations in structured databases · CPC title

• G06F2216/03

  Data mining · CPC title