SQL-based analytic algorithm for rule induction

• US 6,718,322 B1
• Filed: 10/01/1999
• Issued: 04/06/2004
• Est. Priority Date: 10/02/1998
• Status: Expired due to Term

• Current Assignee: Teradata US, Inc. (Teradata Corporation)
• Original Assignee: NCR Corporation
• Inventors: Brye, Todd Michael
• Primary Examiner(s): Corrielus, Jean M.
• Assistant Examiner(s): To, Baoquoc N
• Application Number:
  US09/410,530
• Time in Patent Office: 1,649 Days
• Field of Search: 707/2, 707/3, 707/6, 707/5, 707/4, 707/45, 707/46, 707/47, 707/48, 707/49, 706/45, 706/46, 706/47, 706/48, 706/49, 706/50, 706/51
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

A method, apparatus, and article of manufacture for performing data mining applications in a relational database management system. At least one analytic algorithm for rule induction is performed by a computer, wherein the analytic algorithm for rule induction includes SQL statements performed by the relational database management system directly against the relational database and programmatic iteration. The analytic algorithm for rule induction solves inference problems that are predictive in nature and require a training phase where the programmatic iteration learns various rules based upon data extracted from the relational database by the SQL statements performed by relational database management system. Thereafter, the analytic algorithm for rule induction creates at least one analytic model within the analytic logical data model from data residing in the relational database.

Claims

1. A system for performing data mining applications, comprising:

(a) a computer having one or more data storage devices connected thereto;

(b) a relational database management system, executed by the computer, for managing a relational database storage on the data storage devices; and

(c) at least one analytic algorithm for rule induction performed by the computer, wherein the analytic algorithm for rule induction includes SQL statements performed by the relational database management system directly against the relational database to extract data from the relational database by performing computations, retrieving counts, and ordering the data within the relational database, and programmatic iteration for computing, concurrently and in parallel with the performance of the SQL statements, all possible values needed to determine which attribute should be used to split the data extracted from the relational database by the SQL statements performed by relational database management system, and the analytic algorithm for rule induction creates at least one analytic model within an analytic logical data model from data residing in the relational database.

2. The system of claim 1, wherein the analytic algorithm for rule induction comprises inductive inference.

3. The system of claim 1, wherein the analytic algorithm for rule induction uses a Call-Level Interface (CLI).

4. The system of claim 1, wherein the analytic algorithm for rule induction uses at least one function selected from a group comprising entropy, genie index, and CHAID functions.

5. The system of claim 1, wherein the programmatic iteration performs a training algorithm against the data extracted from the relational database by the SQL statements performed by relational database management system.

8. The system of claim 1, wherein the analytic algorithm for rule induction solves inference problems that are predictive in nature and require a tuning phase.

9. The system of claim 1, wherein the programmatic iteration learns various rules based upon the data extracted from the relational database by the SQL statements performed by relational database management system.

6. A method for performing data mining applications, comprising:

(a) managing a relational database stored on one or more data storage devices connected to a computer; and

(b) performing at least one analytic algorithm for rule induction in the computer, wherein the analytic algorithm for rule induction includes SQL statements performed by the relational database management system directly against the relational database to extract data from the relational database by performing computations, retrieving counts, and ordering the data within the relational database, and programmatic iteration for computing, concurrently and in parallel with the performance of the SQL statements, all possible values needed to determine which attribute should be used to split the data extracted from the relational database by the SQL statements performed by relational database management system, and the analytic algorithm for rule induction creates at least one analytic model within an analytic logical data model from data residing in the relational database.

10. The method of claim 6, wherein the analytic algorithm for rule induction comprises inductive inference.

11. The method of claim 6, wherein the analytic algorithm for rule induction uses a Call-Level Interface (CLI).

12. The method of claim 6, wherein the analytic algorithm for rule induction uses at least one function selected from a group comprising entropy, genie index, and CHAID functions.

13. The method of claim 6, wherein the programmatic iteration performs a training algorithm against the data extracted from the relational database by the SQL statements performed by relational database management system.

14. The method of claim 6, wherein the analytic algorithm for rule induction solves inference problems that are predictive in nature and require a training phase.

15. The method of claim 6, wherein the programmatic iteration learns various rules based upon the data extracted from the relational database by the SQL statements performed by relational database management system.

7. An article of manufacture comprising logic embodying a method for performing data mining applications, comprising:

(a) managing a relational database stored on one or more data storage devices connected to a computer; and

(b) performing at least one analytic algorithm for rule induction in the computer, wherein the analytic algorithm for rule induction includes SQL statements performed by the relational database management system directly against the relational database to extract data from the relational database by performing computations, retrieving counts, and ordering the data within the relational database, and programmatic iteration for computing, concurrently and in parallel with the performance of the SQL statements, all possible values needed to determine which attribute should be used to split the data extracted from the relational database by the SQL statements performed by relational database management system, and the analytic algorithm for rule induction creates at least one analytic model within an analytic logical data model from data residing in the relational database.

16. The article of manufacture of claim 7, wherein the analytic algorithm for rule induction comprises inductive inference.

17. The article of manufacture of claim 7, wherein the analytic algorithm for rule induction uses a Call-Level Interface (CLI).

18. The article of manufacture of claim 7, wherein the analytic algorithm for rule induction uses at least one function selected from a group comprising entropy, genie index, and CHAID functions.

19. The article of manufacture of claim 7, wherein the programmatic iteration performs a training algorithm against the data extracted from the relational database by the SQL statements performed by relational database management system.

20. The article of manufacture of claim 7, wherein the analytic algorithm for rule induction solves inference problems that are predictive in nature and require a training phase.

21. The article of manufacture of claim 7, wherein the programmatic iteration learns various rules based upon the data extracted from the relational database by the SQL statements performed by relational database management system.