Method and computer program product for using data mining tools to automatically compare an investigated unit and a benchmark unit

• US 7,493,324 B1
• Filed: 12/05/2005
• Issued: 02/17/2009
• Est. Priority Date: 12/05/2005
• Status: Expired due to Fees

• Current Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Original Assignee: Verizon Services Corporation (Verizon Communications Inc.)
• Inventors: Drew, James Howard
• Primary Examiner(s): Chace, Christian P
• Assistant Examiner(s): Veillard, Jacques
• Application Number:
  US11/293,242
• Time in Patent Office: 1,170 Days
• Field of Search: 707 1- 6, 707/10, 707/104.1, 707/101, 705/7, 705/10
• US Class Current: N/A
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 90/00 Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
• CPC Class Codes: Y10S 707/99936 Pattern matching access

Abstract

Sources of operational problems in business transactions often show themselves in relatively small pockets of data, which are called trouble hot spots. Identifying these hot spots from internal company transaction data is generally a fundamental step in the problem'"'"'s resolution, but this analysis process is greatly complicated by huge numbers of transactions and large numbers of transaction variables to analyze. A suite of practical modifications are provided to data mining techniques and logistic regressions to tailor them for finding trouble hot spots. This approach thus allows the use of efficient automated data mining tools to quickly screen large numbers of candidate variables for their ability to characterize hot spots. One application is the screening of variables which distinguish a suspected hot spot from a reference set.

Claims

1. A processor implemented method of comparing an investigated entity to a reference entity, the method comprising:

augmenting a plurality of data points that correspond to a variable or characteristic of the investigated entity or the reference entity by creating a target variable whose value is indicative of whether the respective data point is associated with the investigated entity or the reference entity;

performing logistic regression using said processor upon the augmented data points with the target variable used as a dependent variable in performing the logistic regression;

receiving from the logistic regression a plurality of standardized values of regression coefficients for the submitted variables;

identifying variables whose standardized values exceed a specified threshold and are thereby considered significant;

determining a difference between the standardized value of the investigated entity and the standardized values of the reference entity for at least one of the identified variables in order to compare the investigated entity to the reference entity; and

providing an output based upon a comparison of the investigated entity to the reference entity.

2. The method of claim 1, wherein the standardized value of each regression coefficient is a t-value.

3. The method of claim 2, wherein the specified threshold for determining whether a t-value is significant is a corresponding p-value provided by the logistic regression.

4. The method of claim 1, further comprising creating one or more interaction variables from the variables found to be significant in said identifying step, and repeating said performing, said receiving, and said identifying steps with the one or more interaction variables included as part of the augmented data points.

5. The method of claim 1, further comprising creating one or more interaction variables from the variables included in the augmented data points prior to performing the logistic regression.

6. The method of claim 1, further comprising performing pre-processing of the data prior to performing the logistic regression.

7. The method of claim 6, wherein the pre-processing comprises subjecting the data to a decision tree analysis using the target variable created in said augmenting step, and choosing those variables which influence the decision tree for inclusion in the logistic regression.

8. The method of claim 6, wherein the pre-processing comprises at least one of trimming outliers in the data and transforming the data to near symmetry.

9. The method of claim 1, wherein said identifying step comprises ranking the variables in order of standardized value of regression coefficients.

10. The method of claim 1, wherein the investigated entity is a first business unit within a business organization and the reference entity is a second business unit within the business organization.

11. A computer program product, disposed on a computer readable medium, for comparing an investigated entity to a reference entity, the computer program product comprising instructions for causing a processor to:

augment a plurality of data points that correspond to a variable or characteristic of the investigated entity or the reference entity by creating a target variable whose value is indicative of whether the respective data point is associated with the investigated entity or the reference entity;

perform logistic regression upon the augmented data points with the target variable used as a dependent variable in performing the logistic regression;

receive from the logistic regression a plurality of standardized values of regression coefficients for the submitted variables;

identify variables whose standardized values exceed a specified threshold and are thereby considered significant;

determine a difference between the standardized values of the investigated entity and the standardized values of the reference entity for at least one of the identified variables in order to compare the investigated entity to the reference entity; and

provide an output based upon a comparison of the investigated entity to the reference entity.

12. The computer program product of claim 11, wherein the standardized value of each regression coefficient is a t-value.

13. The computer program product of claim 12, wherein the specified threshold for determining whether a t-value is significant is a corresponding p-value provided by the logistic regression.

14. The computer program product of claim 11, further comprising instructions for causing the processor to create one or more interaction variables from the variables found to be significant in said identifying step, and repeating said performing, said receiving, and said identifying steps with the one or more interaction variables included as part of the augmented data points.

15. The computer program product of claim 11, further comprising instructions for causing the processor to create one or more interaction variables from the variables included in the augmented data points prior to performing the logistic regression.

16. The computer program product of claim 11, further comprising instructions for causing the processor to perform pre-processing of the data prior to performing the logistic regression.

17. The computer program product of claim 16, wherein the pre-processing comprises subjecting the data to a decision tree analysis using the target variable created in said augmenting step, and choosing those variables which influence the decision tree for inclusion in the logistic regression.

18. The computer program product of claim 16, wherein the pre-processing comprises at least one of trimming outliers in the data and transforming the data to near symmetry.

19. The computer program product of claim 11, wherein said identifying step comprises ranking the variables in order of standardized value of regression coefficients.

20. The computer program product of claim 11, wherein the investigated entity is a first business unit within a business organization and the reference entity is a second business unit within the business organization.