TECHNIQUES FOR USING DATABASE METRIC RESULTS

• US 20100185602A1
• Filed: 01/09/2009
• Published: 07/22/2010
• Est. Priority Date: 01/09/2009
• Status: Active Grant

• Current Assignee: Teradata Corporation
• Original Assignee: Teradata Corporation
• Inventors: Draper, Linette, Phibbs, Paul H., Ruegsegger, Marianne
• Granted Patent: US 8,595,217 B2
• Time in Patent Office: Days
• US Class Current: 707/721
• CPC Class Codes: G06F 16/24528 Standardisation; Simplification
• CPC Class Codes: G06F 16/2453 Query optimisation
• CPC Class Codes: G06F 16/24535 of sub-queries or views

Abstract

Techniques for using database metric results are provided. Structure Query Language (SQL) statements are parsed for multiple metric calculations. Each metric calculation is dynamically processed against a database to obtain combined results. The combined results are fed to remaining portions of the SQL statements as a source for or a driver to the remaining portions of the SQL statements.

Claims

1. A method implemented in a computer-readable storage medium and processed by a processor to perform the method, comprising:

receiving Structured Query Language (SQL) statements;

identifying a first metric calculation and a second metric calculation in the SQL statements, wherein the first metric calculation and the second metric calculation are a source for a target rule within the SQL statements; and

processing the first metric calculation and the second metric calculation against a database and using results as the source that is fed to the target rule within the SQL statements, and wherein the target rule is processed with the results.

2. The method of claim 1, wherein receiving further includes interacting with a user via a graphical user interface to receive the SQL statements.

3. The method of claim 1, wherein identifying further includes parsing the SQL statements to identify a first rule that defines the first metric calculation and to identify a second rule that defines the second metric calculation.

4. The method of claim 1, wherein identifying further includes identifying the first metric calculation as a metric for other revenue, indirect expense, direct expense, allocated capital, or risk provision.

5. The method of claim 4, wherein identifying further includes identifying the second metric calculation as a different metric from that which is associated with the first metric calculation.

6. The method of claim 1, wherein identifying further includes recognizing two different rules that define the first metric calculation within the SQL statements.

7. The method of claim 1, wherein processing further includes summing the first metric calculation and the second metric calculation to obtain the results that are provided as the source to the target rule when processing the SQL statements.

8. A method implemented in a computer-readable storage medium and processed by a processor to perform the method, comprising:

dynamically parsing Structured Query Language (SQL) statements to obtain a first metric calculation and a second metric calculation;

processing the first metric calculation and the second metric calculation against a database and summing first results from the first metric calculation with second results from the second metric calculation to produce combined results; and

using the combined results as input to other portions of the SQL statements when processing the other portions against the database.

9. The method of claim 8, wherein dynamically parsing further includes recognizing the first metric calculation as a first rule that is to be evaluated when the first metric calculation is processed.

10. The method of claim 9, wherein dynamically parsing further includes recognizing the second metric calculation as a second rule that is to be evaluated when the second metric calculation is processed.

11. The method of claim 8, wherein processing further includes producing the combined results as a source to the other portions of the SQL statements.

12. The method of claim 8, wherein processing further includes producing the combined results as a driver to the other portions of the SQL statements.

13. The method of claim 8, wherein processing further includes processing a first rule chain representing a set of rules when calculating the first metric calculation.

14. The method of claim 9, wherein processing further includes processing a second rule chain representing another set of rules when calculating the second metric calculation.

15. A computer-implemented system that executes on one or more processors, the system comprising:

a Structured Query Language (SQL) preprocessor implemented in a computer-readable storage medium and executed by a processor of a network; and

a data store implemented in a computer-readable storage medium and accessible to the SQL preprocessor;

wherein the SQL preprocessor iterates SQL statements to automatically and dynamically identify and process multiple metric calculations against the database to produce combined results that feed other portions of the SQL statements when those other portions of the SQL statements are processed.

16. The system of claim 15, wherein the SQL preprocessor iterates the SQL statements to process multiple sets of rules for each of the metric calculations.

17. The system of claim 15, wherein the SQL preprocessor feeds the combined results as a source input to a rule that represents the other portions of the SQL statements.

18. The system of claim 15, wherein the SQL preprocessor feeds the combined results as a driver to the remaining portions of the SQL statements.

19. The system of claim 15, wherein the SQL preprocessor interacts with a user via a graphical user interface to interactively acquire the SQL statements.

20. The system of claim 15, wherein data store is a data warehouse that includes a collection of databases logically organized and accessible as a single unit.