CREATING AND MANAGING A POLICY CONTINUUM

• US 20080162109A1
• Filed: 12/28/2006
• Published: 07/03/2008
• Est. Priority Date: 12/28/2006
• Status: Abandoned Application

• Current Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Original Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Inventors: Raymer, David L., Strassner, John C.
• Application Number:
  US11/617,369
• Publication Number:
  US 20080162109A1
• Time in Patent Office: Days
• US Class Current: 704/1
• CPC Class Codes: G06F 40/143 Markup, e.g. Standard Generalized Markup Language [SGML] or Document Type Definition [DTD]
• CPC Class Codes: G06F 40/151 Transformation
• CPC Class Codes: G06F 40/247 Thesauruses; Synonyms

Abstract

A method and system for managing a policy includes a memory adapted to store at least one policy, residing at a first abstraction level, where each policy includes a plurality of language elements in a first vocabulary set and a processor communicatively coupled to the memory and adapted to perform semantic resolution using a set of ontologies by mapping at least one of the language elements in the first vocabulary set to at least one of a plurality of language elements in a second vocabulary set, the second vocabulary set having at least one of a different grammar and a different vocabulary from the first vocabulary set create a second policy, in response to the semantic resolution.

Claims

1. A method for managing a policy, the method comprising:

accessing at least one policy, residing at a first abstraction level, where each policy includes a plurality of language elements in a first vocabulary set;

performing semantic resolution using a set of ontologies by mapping at least one of the language elements in the first vocabulary set to at least one of a plurality of language elements in a second vocabulary set, the second vocabulary set having at least one of a different grammar and a different vocabulary from the first vocabulary set; and

in response to the semantic resolution, creating a second policy that is related to the first policy.

2. The method of claim 1, further comprising:

performing syntactic resolution by mapping at least one of the language elements in the first vocabulary set to at least one of the language elements in the second vocabulary set using an information model associated with both the policy and the language elements in each of the first vocabulary set and the second vocabulary set.

3. The method of claim 2, further comprising:

performing syntactic resolution by mapping at least one of the language elements in the first vocabulary set to at least one of the language elements in the second vocabulary set using a data model associated with both the policy as well as the language elements in each of the first vocabulary set and the second vocabulary set.

4. The method of claim 1, wherein the semantic resolution includes prompting a user for input when assigning the language elements in the second vocabulary set to a level of abstraction.

5. The method of claim 4, wherein the level of abstraction is selected by the user.

6. The method of claim 1, wherein the semantic resolution includes assigning at least one subset of the language elements in the second vocabulary set to a new policy residing at one of the first abstraction level, the second abstraction level, or a third abstraction level, thereby splitting the policy into two policies.

7. The method of claim 1, wherein the semantic resolution includes using at least one of a matching function and a semantic equivalence function;

wherein the matching function is based on one or more of syntax, keywords, metadata, and other language elements, that identifies words and phrases in a language that the policy uses with model elements from at least one of an information and a data model and concepts in the set of ontologies;

wherein the semantic equivalence function produces a ratioed result thereby enabling different matches to be placed in rank-order; and

wherein the semantic resolution provides preferred choices from the vocabulary used in the policy to define which words or phrases in the second vocabulary should be used to match the intent of the words or phrases in the first vocabulary.

8. The method of claim 1, further comprising:

removing at least one of the language elements in the first vocabulary set based upon at least one of a lexical class in the language of the policy such that removal of the language element does not alter a meaning of the policy within a specified level of abstraction.

9. The method of claim 1, further comprising:

removing a set of language elements in the first vocabulary set based on pattern matching to at least one of a template for syntactical replacement, at least one concept in at least one ontology of the set of ontologies, and at least one model element in at least one of an information and a data model.

10. The method of claim 1, further comprising:

using a combination of syntactic and semantic resolution to jointly determine the set of language elements from the first vocabulary set to map to a set of language elements from a second vocabulary set, thereby creating a second policy.

20. The system of claim 1, wherein the processor is further adapted to:

remove a set of language elements in the first vocabulary set based on pattern matching to at least one of a template for syntactical replacement, at least one concept in at least one ontology of the set of ontologies, and at least one model element in at least one of an information and a data model.

21. The system of claim 1, wherein syntactic resolution is performed by mapping at least one of the language elements in the first vocabulary set to at least one of the language elements in the second vocabulary set using two or more data models associated with both the policy as well as the language elements in each of the first vocabulary set and the second vocabulary set;

the differing definitions found in the two or more data models are resolved using the single information model.

22. The system of claim 1, wherein semantic resolution is performed by mapping at least one of the language elements in the first vocabulary set to at least one of the language elements in the second vocabulary set using two or more data models associated with both the policy as well as the language elements in each of the first vocabulary set and the second vocabulary set;

the differing definitions found in the two or more data models are resolved using the single information model in conjunction with one or more ontologies.

11. A method for managing a policy, the method comprising:

modeling at least a first set of policies, all at one policy continuum level, as a set of mathematical operations that describe possible computations of each of the first set of policies;

modeling at least a second set of policies, all at a different policy continuum level than that of the first set of policies, as a set of mathematical operations that describe possible computations of each of the second set of policies; and

analyzing both the first set of policies and the second set of policies to ensure that a mapping between the mathematical operations of the first set of policies and the mathematical operations of the second set of policies exists without producing a conflict therebetween.

12. The method of claim 11, further comprising:

forming a complete policy continuum by accepting a policy which is at any level of the policy continuum and iteratively translating the policy to higher and lower levels of abstraction.

13. A system for managing a policy, the system comprising:

a memory adapted to store at least one policy, residing at a first abstraction level, where each policy includes a plurality of language elements in a first vocabulary set;

a processor communicatively coupled to the memory and adapted to;

perform semantic resolution using a set of ontologies by mapping at least one of the language elements in the first vocabulary set to at least one of a plurality of language elements in a second vocabulary set, the second vocabulary set having at least one of a different grammar and a different vocabulary from the first vocabulary set; and

create a second policy, in response to the semantic resolution.

14. The system of claim 13, wherein the processor is further adapted to:

perform syntactic resolution by mapping at least one of the language elements in the first vocabulary set to at least one of the language elements in the second vocabulary set using a data model associated with both the policy as well as the language elements in each of the first vocabulary set and the second vocabulary set.

15. The system of claim 13, wherein the semantic resolution includes prompting a user for input when assigning the language elements in the second vocabulary set to a level of abstraction.

16. The system of claim 15, wherein the level of abstraction is selected by the user.

17. The system of claim 13, wherein the semantic resolution includes assigning at least one subset of the language elements in the second vocabulary set to a new policy residing at one of the first abstraction level, the second abstraction level, or a third abstraction level, thereby splitting the policy into two policies.

18. The system of claim 13, wherein the semantic resolution includes using at least one of a matching function and a semantic equivalence function;

wherein the matching function is based on one or more of syntax, keywords, metadata, and other language elements, that identifies words and phrases in a language that the policy uses with model elements from at least one of an information and a data model and concepts in the set of ontologies;

wherein the semantic equivalence function produces a ratioed result thereby enabling different matches to be placed in rank-order; and

wherein the semantic resolution provides preferred choices from the vocabulary used in the policy.

19. The system of claim 13, wherein the processor is further adapted to:

remove at least one of the language elements in the first vocabulary set based upon at least one of a lexical class in the language of the policy such that removal of the language element does not alter a meaning of the policy within a specified level of abstraction.