Node similarity for component substitution

• US 20120185422A1
• Filed: 01/14/2011
• Published: 07/19/2012
• Est. Priority Date: 01/14/2011
• Status: Active Grant

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Shah, Amip J., Marwah, Manish
• Granted Patent: US 8,626,693 B2
• Time in Patent Office: Days
• US Class Current: 706/47
• CPC Class Codes: G06F 16/24558 Binary matching operations
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence

Abstract

Systems and methods of determining node similarity for component substitution. An example of a method may be carried out by program code stored on non-transient computer-readable medium and executed by a processor. The method includes estimating a normalized similarity metric between the plurality of nodes in the system tree and nodes in other trees. The method also includes assigning a similarity score to each compared node using at least one of: domain based rules, attribute based similarity metrics, and machine learning. The method also includes combining results for cluster analysis.

Claims

1. A method of determining node similarity for component substitution, the method carried out by program code stored on non-transient computer-readable medium and executed by a processor, the method comprising:

estimating a normalized similarity metric between the plurality of nodes in the system tree and nodes in other trees;

assigning a similarity score to each compared node using at least one of;

domain based rules, attribute based similarity metrics, and machine learning; and

combining results for cluster analysis.

2. The method of claim 1, further comprising rating the nodes based on respective similarity scores, and replacing the at least one of the common nodes in the system tree is based on the rating.

3. The method of claim 1, wherein domain-based rules uses domain knowledge to construct a set of rules based on node attributes

4. The method of claim 1, wherein domain-based rules include heuristics related to at least one of databases, nodes within the databases, and contextual information.

5. The method of claim 1, wherein attribute based similarity metrics are based on multivariate attributes for each node.

6. The method of claim 5, wherein the multivariate attributes include at least one of LCS, LCP, Levenshtein distance, Minkowski distance, weights assigned to each attribute, and dot product between two attribute vectors.

7. The method of claim 1, wherein model based similarity is determined based on available node data.

8. The method of claim 1, wherein the model based similarity uses at least one of textual descriptions of each node, data where similarity between nodes is known, topic models, and contextual metrics.

9. The method of claim 1, wherein the similarity score is used to compute similarity between any two nodes in a database.

10. A system determining node similarity for component substitution, comprising:

a computer readable storage to store at least one system tree having a plurality of nodes, each of the plurality of nodes in the system tree representing a characteristic of a component of a system under consideration; and

an analysis engine operatively associated with the computer readable storage to estimate a normalized similarity metric between the plurality of nodes in the system tree and nodes in other trees, the analysis engine assigning a similarity score to each compared node using at least one of;

domain based rules, attribute based similarity metrics, and machine learning.

11. The system of claim 10, wherein the analysis engine rates the nodes based on respective similarity scores, and replacing the at least one of the common nodes in the system tree is based on the rating.

12. The system of claim 10, wherein a similarity score of one indicates interchangeable nodes, and a similarity score of zero indicates no match.

13. The system of claim 10, wherein the similarity score is used for cluster analysis.

14. The system of claim 10, wherein domain-based rules uses domain knowledge to construct a set of rules based on node attributes.

15. The system of claim 10, wherein attribute based similarity metrics is determined based on multivariate attributes for each node.

16. The system of claim 10, wherein model based similarity is determined based on available node data.

17. The system of claim 10, wherein the similarity score is used to compute similarity between any two nodes in a database.

19. The system of claim 10, wherein the similarity score is used for cluster analysis.

18. A system of determining node similarity for component substitution, comprising:

an analysis engine operatively associated with a computer readable storage to estimate a normalized similarity metric between the plurality of nodes in the system tree and nodes in other trees;

a similarity score assigned to each compared node using at least one of;

domain based rules, attribute based similarity metrics, and machine learning.

20. The system of claim 18, further comprising the analysis engine rating the nodes based on respective similarity scores, and the analysis engine replacing the at least one of the common nodes in the system tree is based on the rating.