System and method for generating and using a pooled knowledge base

• US 20050033761A1
• Filed: 03/04/2004
• Published: 02/10/2005
• Est. Priority Date: 03/04/2003
• Status: Abandoned Application

• Current Assignee: Carnegie Mellon University
• Original Assignee: Carnegie Mellon University
• Inventors: Rosenoer, Jonathan, Guttman, William
• Application Number:
  US10/793,110
• Publication Number:
  US 20050033761A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A method of dynamically creating a database is comprised of receiving event data from a plurality of independent agents input according to a common taxonomy that exposes the event in its molecular terms, e.g., causal factors driving the event and mitigating factors related to the event, and storing the event data. The molecular terms may be weighted. Additionally, the agents inputting the event data may be authenticated to insure that data is being entered by only those parties authorized to do so. The event data may also be validated by reference to external sources of information. The event data may additionally be normalized, anonymized and scaled. Synthetic event data may be added to the database for those situations where actual data is not available or is not very comprehensive. The synthetic event data may be generated by one of a test bed or a subject matter expert. After the database is created, a search engine or analytic engine may operate on the data to provide various reports such as root cause, failure, what-if, among others. Because of the rules governing abstracts, this abstract should not be used in construing the claims.

Claims

1. A method of dynamically creating a database, comprising:

receiving event data from a plurality of independent agents input according to a common taxonomy that exposes the event in its molecular terms; and

storing the event data.

2. The method of claim 1 wherein said receiving data includes receiving causal factors driving the event and mitigating factors related to the event, said causal factors and mitigating factors are weighted.

3. The method of claim 1 additionally comprising authenticating the agent from which the event data is received.

4. The method of claim 1 additionally comprising validating the event data.

5. The method of claim 1 additionally comprising normalizing the event data.

6. The method of claim 1 additionally comprising anonymizing the event data.

7. The method of claim 1 additionally comprising scaling the event data.

8. The method of claim 1 additionally comprising adding synthetic event data to the database.

9. The method of claim 8 wherein said synthetic event data is generated by one of a test bed or a subject matter expert.

10. A method of dynamically creating a pooled knowledge base, comprising:

receiving event data from a plurality of independent agents;

decomposing the event data into its molecular terms including at least one weighted causal factor; and

forwarding the event data for storage.

11. The method of claim 10 additionally comprising authenticating the agent from which the event data is received.

12. The method of claim 10 additionally comprising validating the event data.

13. The method of claim 10 additionally comprising normalizing the event data.

14. The method of claim 10 additionally comprising anonymizing the event data.

15. The method of claim 10 additionally comprising scaling the event data.

16. The method of claim 10 additionally comprising adding synthetic event data to the knowledge base.

17. The method of claim 16 wherein said synthetic event data is generated by one of a test bed or a subject matter expert.

18. A method of dynamically generating an aggregate database, comprising:

collecting event data including weighted casual factors and weighted mitigating factors;

normalizing the event data;

anonymizing the event data; and

storing the event data in a repository.

19. The method of claim 18 additionally comprising validating the event data.

20. The method of claim 18 additionally comprising adding synthetic data to the event data in the repository.

21. The method of claim 20 wherein said synthetic data is generated by one of a test bed and a subject matter expert.

22. A computer readable medium encoded with a computer program which, when executed, performs the method comprising;

receiving event data from a plurality of independent agents input according to a common taxonomy that exposes the event in its molecular terms; and

storing the event data.

23. A computer readable medium encoded with a computer program which, when executed, performs the method comprising;

receiving event data from a plurality of independent agents;

decomposing the event data into its molecular terms including at least one weighted causal factor; and

forwarding the event data for storage.

24. A computer readable medium encoded with a computer program which, when executed, performs the method comprising;

collecting event data including weighted casual factors and weighted mitigating factors;

normalizing the event data;

anonymizing the event data; and

storing the event data in a repository.

25. A method of operating on a pooled knowledge base comprised of event data and its molecular components to produce one of a risk report, optimization report, resource allocation report, failure prediction report, root cause report, and what if report.

26. A method of operating on a pooled knowledge base comprised of loss event data and its molecular components to produce one of an aggregate loss distribution, a point loss benchmark, an alert, a report and a simulated capital charge.