Suggesting data interpretations and patterns for updating policy documents

• US 8,229,903 B2
• Filed: 12/19/2002
• Issued: 07/24/2012
• Est. Priority Date: 12/19/2002
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Baffes, Paul T., Garrison, John Michael, Gilfix, Michael, Hsu, Allan, Stading, Tyron Jerrod
• Primary Examiner(s): COLAN, GIOVANNA B
• Application Number:
  US10/324,514
• Publication Number:
  US 20040122823A1
• Time in Patent Office: 3,505 Days
• Field of Search: 707/6, 707/9, 707/101, 707/102, 707/3, 707/1, 707/2, 707/5, 707/661, 707/694, 707/634, 713/201, 714/39, 705/4, 705/1, 726/1, 704/200, 709/203
• US Class Current: 707/694
• CPC Class Codes: G06F 16/30 of unstructured textual data document management systems G06F16/93
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

A system and method for utilizing data mining to generate a policy document or to revise theory within a policy document. A data base of unknown events is mined for application to the development of a system management policy document. The results of the data mining of the database of unknown events are automatically incorporated into a policy document, subject to user approval, to produce a new policy document or an updated version of an existing policy document.

Claims

1. A method for automatic interpretation of system data utilized to create a policy document, said method comprising:

tracking events occurring on a system represented by system data said events including known events represented within the policy document and unknown events;

storing said unknown events in a database of unknown events, wherein only unknown events, separate from known events, are stored in the database of unknown events;

filtering through each unknown event;

analyzing said unknown events within said database;

identifying trends within said database of unknown events based on a historical treatment of said unknown events;

selectively determining which unknown events among all of the unknown events should be considered for inclusion in said policy document; and

updating the policy document with event examples associated with the unknown events selected for inclusion.

2. The method of claim 1, further comprising:

outputting suggested elements from said database for inclusion in said policy documents, wherein said suggested elements are selected and outputted by a software-implemented process.

3. The method of claim 1, wherein said database is a text file configured with a single event per line of text, said method further comprising:

converting said text to a code form that is readable by a clustering algorithm, wherein the clustering algorithm groups into clusters examples of events that are similar to each other; and

passing said code foam to said clustering algorithm.

4. The method of claim 1, further comprising:

grouping similar elements of said database into subgroups; and

generating a feature vector for said subgroups with an associated set of valid values.

5. The method of claim 3, wherein said converting step converts events in the database into examples for said clustering algorithm.

6. The method of claim 3, wherein said converting step is completed by a parser function and includes:

dividing each line of the database into tokens, each token being a word that is separated by a space, wherein each line within said database is split into words separated by a blank space;

generating a dictionary of tokens comprising a single occurrence of each token, wherein duplication of tokens within the dictionary of tokens is substantially avoided; and

converting each line of said database into a token vector with one entry for each token within the line.

7. The method of claim 6, further comprising:

determining a length of the line with a greatest number of tokens;

assigning the length of the line with the greatest number of tokens as the length of each token vector to be utilized to convert events of the database into examples for said clustering algorithm, wherein each token vector is created with said length.

8. The method of claim 6, further comprising:

combining similar event examples into clusters;

outputting said clusters to a user; and

enabling user manipulation of parameters of said clustering algorithm to produce different clusters from those clusters originally outputted.

9. The method of claim 7, further comprising grouping similar expressions into a common token expression, wherein IP addresses are provided with a single, surrogate general expression.

10. A computer system for automatic interpretation of system data utilized to create a policy document, said system comprising:

a processor; and

program code on a computer readable medium and which is executed by the processor to complete the automatic interpretation of system data utilized to create the policy document, said program code comprising;

program code for tracking events occurring on a system represented by system data said events including known events represented within the policy document and unknown events;

program code for filtering through each unknown event;

program code for storing said unknown events in a database of unknown events, wherein only unknown events, separate from known events, are stored in the database of unknown events;

program code for analyzing said unknown events within said database;

program code for identifying trends within said database of unknown events based on a historical treatment of said unknown events;

program code for selectively determining which unknown events among all of the unknown events should be considered for inclusion in said policy document; and

program code for updating the policy document with event examples associated with the unknown events selected for inclusion.

11. The system of claim 10, further comprising:

program code for outputting suggested elements from said database for inclusion in said policy documents, wherein said suggested elements are selected and outputted by a software-implemented process.

12. The system of claim 10, wherein said database is a text file configured with a single event per line of text.

13. The system of claim 12, further comprising:

program code for converting said text to a code form that is readable by a clustering algorithm wherein the clustering algorithm groups into clusters examples of events that are similar to each other; and

program code for passing said code form to said clustering algorithm.

14. The system of claim 13, further comprising:

program code for grouping similar elements of said database into subgroups; and

program code for generating a feature vector for said subgroups with an associated set of valid values.

15. The system of claim 13, wherein said program code for converting converts events in the database into examples for said clustering algorithm.

16. The system of claim 15, wherein said program code for converting comprises a parser function and includes:

program code for dividing each line of the database into tokens, each token being a word that is separated by a space, wherein each line within said database is split into words separated by a blank space;

program code for generating a dictionary of tokens comprising a single occurrence of each token, wherein duplication of tokens within the dictionary of tokens is substantially avoided; and

program code for converting each line of said database into a token vector with one entry for each token within the line.

17. The system of claim 16, further comprising:

program code for determining a length of the line with a greatest number of tokens;

program code for assigning the length of the line with the greatest number of tokens as the length of each token vector to be utilized to convert events of the database into examples for said clustering algorithm, wherein each token vector is created with said length.

18. The system of claim 16, further comprising:

program code for combining similar event examples into clusters;

program code for outputting said clusters to a user; and

program code for enabling user manipulation of parameters of said clustering algorithm to produce different clusters.

19. The system of claim 17, further comprising program code for grouping similar expressions into a common token expression, wherein IP addresses are provided with surrogate general expressions.

20. A computer program product, comprising:

a computer readable medium; and

program code on said computer readable medium for implementing automatic interpretation of system data utilized to create a policy document, said program code comprising code for;

tracking events occurring on a system represented by system data said events including known events represented within the policy document and unknown events;

filtering through each unknown event;

storing said unknown events in a database of unknown events, wherein only unknown events, separate from known events, are stored in the database of unknown events;

analyzing said unknown events within said database;

identifying trends within said database of unknown events based on a historical treatment of said unknown events;

selectively determining which unknown events among all of the unknown events should be considered for inclusion in said policy document; and

updating the policy document with event examples associated with the unknown events selected for inclusion.

21. The computer program product of claim 20, further comprising program code for outputting suggested elements from said database for inclusion in said policy documents, wherein said suggested elements are selected and outputted by a software-implemented process.

22. The computer program product of claim 20, wherein said database is a text file configured with a single event per line of text.

23. The computer program product of claim 22, further comprising program code for:

converting said text to a code form that is readable by a clustering algorithm, wherein the clustering algorithm groups into clusters examples of events that are similar to each other; and

passing said code form to said clustering algorithm.

24. The computer program product of claim 23, further comprising program code for:

grouping similar elements of said database into subgroups; and

generating a feature vector for said subgroups with an associated set of valid values.

25. The computer program product of claim 23, wherein said program code for said converting step converts events in the database into examples for said clustering algorithm.

26. The computer program product of claim 25, wherein said program code for said converting feature comprises a software parser function, which includes program code for:

dividing each line of the database into tokens, each token being a word that is separated by a space, wherein each line within said database is split into words separated by a blank space;

generating a dictionary of tokens comprising a single occurrence of each token, wherein duplication of tokens within the dictionary of tokens is substantially avoided; and

converting each line of said database into token vector with one entry for each token within the line.

27. The computer program product of claim 26, further comprising program code for:

determining a length of the line with a greatest number of tokens; and

assigning the length of the line with the greatest number of tokens as the length of each token vector to be utilized to convert events of the database into examples for said clustering algorithm, wherein each token vector is created with said length.

28. The computer program product of claim 26, further comprising program code for:

combining similar event examples into clusters;

outputting said clusters to a user; and

enabling user manipulation of parameters of said clustering algorithm to produce different clusters.

29. The computer program product of claim 27, further comprising program code for grouping similar expressions into a common token expression, said grouping code including code for replacing IP addresses with a single, surrogate general expression.