Method and system for automatically building intelligent reasoning models based on Bayesian networks using relational databases

• US 20070143338A1
• Filed: 12/21/2005
• Published: 06/21/2007
• Est. Priority Date: 12/21/2005
• Status: Abandoned Application

• Current Assignee: The Boeing Co.
• Original Assignee: The Boeing Co.
• Inventors: Wang, Guijun, Wang, Changzhou, Chen, Alice, Wang, Haiqin
• Application Number:
  US11/314,845
• Publication Number:
  US 20070143338A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Method and system of building a reasoning model using relational databases is provided. The method includes identifying data objects in relational databases; determining dependency relationships between the data objects; translating the data objects into nodes of a Bayesian network; and automatically translating the dependency relationships into a graphical structure of a Bayesian network. The system includes at least one server for storing data of a system having numerous interconnected parts; monitoring agents for monitoring the data of the numerous interconnected parts stored in the system; an events log for storing any event observed by the monitoring agents; and relational databases for storing data objects, the data objects correspond to the data of the numerous interconnected parts.

Claims

1. A method of building a reasoning model using relational databases, comprising:

Identifying data objects in the relational databases;

Determining dependency relationships between the data objects;

Translating the data objects into nodes of a Bayesian network; and

Automatically translating the dependency relationships into a graphical structure of a Bayesian network.

2. The method of claim 1, wherein the data objects are identified relative to a reasoning task from multiple tables in the relational databases.

3. The method of claim 1 further comprising computing a frequency of events'"'"' occurrence to estimate probability distribution for nodes.

4. The method of claim 1 further comprising performing intelligent reasoning based on the network.

5. The method of claim 1 wherein the Bayesian network is comprised of five columns.

6. The method of claim 5, wherein the first column represents host computers.

7. The method of claim 5, wherein the second column represents web applications.

8. The method of claim 5, wherein the third column represents monitoring agents.

9. The method of claim 5, wherein the fourth and fifth columns represent observation nodes.

10. The method of claim 1 further comprising issuing an alert upon the occurrence of an event.

11. The method of claim 10, wherein alerts are classified as critical, warning or normal.

12. The method of claim 1 further comprising

monitoring data using monitoring agents;

and

generating observations nodes based upon the monitored data.

13. The method of claim 1 further comprising computing posterior probability based on observations or partial observations.

14. The method of claim 1, wherein monitored data is stored in an events log.

15. A system of building a reasoning model using relational databases,comprising:

At least one server for storing data of a system having numerous interconnected parts;

Monitoring agents for monitoring the data of the numerous interconnected parts stored in the system;

An events log for storing any event observed by the monitoring agents; and

Relational databases for storing data objects, the data objects correspond to the data of the numerous interconnected parts.

16. The system of claim 15, wherein an event includes any type of occurrence in the system.

17. The system of claim 16, wherein an occurrence includes a failure of a system component or the delivery of information.

18. The system of claim 15, wherein the at least one server is a host computer.

19. The system of claim 15 wherein dependency relationships between the data objects are determined.

20. The system of claim 19, wherein the data objects are translated into nodes of a Bayesian network

21. The system of claim 20, wherein the dependency relationships are automatically translated into a graphical structure of a Bayesian network.