Method and system for processing rules using an extensible object-oriented model resident within a repository

• US 6,415,275 B1
• Filed: 08/05/1999
• Issued: 07/02/2002
• Est. Priority Date: 08/05/1999
• Status: Expired due to Term

• Current Assignee: Unisys Corporation
• Original Assignee: Unisys Corporation
• Inventors: Zahn, Karl Theodore
• Primary Examiner(s): Black, Thomas
• Assistant Examiner(s): Booker, Kelvin
• Application Number:
  US09/368,785
• Time in Patent Office: 1,062 Days
• Field of Search: 706/47, 706/48, 706/45
• US Class Current: 706/47
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems

Abstract

A method for processing rules using a model resident within a persistent storage means of a computer system executing a repository program. The method includes the steps of storing in the persistent storage means the model, which comprises a set of rules and a set of facts defining event states. Both of these are stored as objects. The set of rules and set of facts comprise an object-oriented representation of the model. Next, a set of commands representing an extensible inference engine are loaded as meta objects into the persistent storage. The method accepts as inputs events, which represent alterations in the set of facts and determines which of the rules is associated with the alterations in the set of facts. The most salient one of the rules determined in the preceding step is then selected and executed.

Claims

1. In a computer system executing a repository program and having a persistent storage means, a method for processing rules using a model resident within said storage means, said method comprising the steps of:

a. storing in said persistent storage means said model comprising a set of rules and a set of facts defining event states, both of which are stored as objects, wherein said set of rules and said set of facts comprise an object-oriented representation of said model;

b. loading as meta objects in said persistent storage a set of commands representing an extensible inference engine;

c. accepting as inputs to said method events representing alterations in said set of facts;

d. determining which of said rules is associated with said alterations in said set of facts;

e. selecting the most salient one of said rules determined in the preceding step; and

,

f. executing said most salient rule selected in the preceding step, said executing further comprising implementing the right-hand side of the rule selected in step e.

2. The method as in claim 1 wherein any event change is sensed and processed to check if there are any rules affected, wherein a subsequent implementation of a rule results in an action.

3. The method as in claim 2 wherein said inputs comprise events occurring as result of said action.

4. The method as in claim 1 further including the step of tracing all rules executed and conclusions reached.

5. The method as in claim 1 wherein said step of loading as meta objects includes defining operations that may be adapted by a user for customizing execution of said method.

6. The method as in claim 1 wherein said step of accepting as inputs further includes sensing changes in system state.

7. The method as in claim 1 wherein said inputs comprise events occurring in software programs being monitored by said method.

8. The method as in claim 1 wherein said inputs comprise events occurring in a telemetry stream being monitored by said method.

9. The method as in claim 1 wherein said inputs comprise events occurring in a computer system being monitored by said method.

10. The method as in claim 1 wherein said step of selecting further comprises sorting in order of saliency rules which have been satisfied.

11. The method as in claim 10 wherein said step of sorting further includes determining from those rules with the highest saliency which rule was added last to list of satisfied rules and using this last rule for execution.

12. The method as in claim 1 wherein said step of storing said model further comprises importing said model from a UML-based modeling tool as a standard XML file.

13. The method as in claim 1 wherein said step of storing said model further comprises entering said rules from a rule editing interface, which may be easily adapted to any end user.

14. The method as in claim 1 further comprising the step of interfacing said method through an open API so that any client application can be written or modified for accessing functionality of said method.

15. A storage medium encoded with machine-readable computer program code for processing rules in a computer system executing a repository program and having a persistent storage means, using a model resident within said storage means, wherein, when the computer program code is executed by a computer, the computer performs the steps of:

a. storing in said persistent storage means said model comprising a set of rules and a set of facts defining event states, both of which are stored as objects, wherein said set of rules and said set of facts comprise an object-oriented representation of said model;

b. loading as meta objects in said persistent storage a set of commands representing an extensible inference engine;

c. accepting as inputs to said method events representing alterations in said set of facts;

d. determining which of said rules is associated with said alterations in said set of facts;

e. selecting the most salient one of said rules determined in the preceding step; and

,

f. executing said most salient rule selected in the preceding step, said executing further comprising implementing the right-hand side of the rule selected in step e.

16. A storage medium as in claim 15 wherein any event change is sensed and processed to check if there are any rules affected, wherein a subsequent implementation of a rule results in an action.

17. A storage medium as in claim 16 wherein said inputs comprise events occurring as a result of said action.

18. A storage medium as in claim 15 further including the step of tracing all rules executed and conclusions reached.

19. A storage medium as in claim 15 wherein said step of loading as meta objects includes defining operations that may be adapted by a user for customizing execution of said method.

20. A storage medium as in claim 15 wherein said step of accepting as inputs further includes sensing changes in system state.

21. A storage medium as in claim 15 wherein said inputs comprise events occurring in software programs being monitored by said method.

22. A storage medium as in claim 15 wherein said inputs comprise events occurring in a telemetry stream being monitored by said method.

23. A storage medium as in claim 15 wherein said inputs comprise events occurring in a computer system being monitored by said method.

24. A storage medium as in claim 15 wherein said step of selecting further comprises sorting in order of saliency rules which have been satisfied.

25. A storage medium as in claim 24 wherein said step of sorting further includes determining from those rules with the highest saliency which rule was added last to listed of satisfied rules and using this rule for execution.

26. A storage medium as in claim 15 wherein said step of executing further comprises implementing the right-hand side of the rule selected in step e thereof.

27. A storage medium as in claim 15 wherein said step of storing said model further comprises importing said model from a UML-based modeling tool as a standard XML file.

28. A storage medium as in claim 15 wherein said step of interfacing said method through an open API so that any client application can be written or modified for accessing functionality of said method.