System and methods for data analysis and trend prediction

• US 20060112111A1
• Filed: 03/22/2005
• Published: 05/25/2006
• Est. Priority Date: 11/22/2004
• Status: Abandoned Application

• Current Assignee: NEC Laboratories America Inc (NEC Corporation)
• Original Assignee: NEC Laboratories America Inc (NEC Corporation)
• Inventors: Tseng, Belle, Wu, Yi
• Application Number:
  US11/086,172
• Publication Number:
  US 20060112111A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound

Abstract

Systems and methods for data analysis and trend prediction. Multiple networks are combined for analysis to improve the accuracy of the evaluation by broadening the type of criteria considered. Relevant features are extracted from a dataset and at least one network is formed representing various relationships identified among the items contained in the dataset according to heuristics. Statistical analyses are applied to the relationships and the results output to a user via one or more reports to permit a user to evaluate each of the items in the dataset relative to each other. The trend of the relationships may be predicted based on the results of statistical analysis applied to the features over successive discrete time periods.

Claims

1. A computer-implemented method comprising:

generating one or more nodes using feature extraction from a dataset, wherein each node represents a concept; and

determining at least a first relationship among the nodes;

wherein the generating is accomplished based on heuristics using the first relationship.

2. The method of claim 1, wherein the heuristics includes an impact profile.

3. The method of claim 2, further comprising:

generating the impact profile for each of a plurality of items based on information associated with the items obtained from the dataset;

generating an expertise profile for each of the plurality of items based on the impact profile; and

outputting a report representing the contents of the impact profile and expertise profile, wherein the report indicates a relative ranking of the items based on the contents of the impact profile and the expertise profile.

4. The method of claim 3, wherein the generating one or more nodes is accomplished by forming a query to extract items having a candidate profile most nearly matching the expertise profile.

5. The method of claim 3, further comprising:

determining a second relationship between the nodes based on metadata associated with the items in the dataset.

6. The method of claim 5, further comprising:

generating a social profile for each of the plurality of items based on the second relationship;

wherein the impact profile is formed as a linear combination of the first relationship and the second relationship; and

wherein the report represents the contents of the impact profile, the expertise profile, and the social profile, and wherein the ranking is based on the contents of the impact profile, the expertise profile, and the social profile.

7. The method of claim 6, wherein the generating one or more nodes is accomplished by forming a query to extract items having a candidate profile most nearly matching a linear combination of the expertise profile and the social profile.

8. The method of claim 7, in which the linear combination is defined as:

Sim(Q,D)=β

*Sim(Q_E,(D_R,D_E))+(1−

β

)*Sim(Q_s,D_S).

9. The method of claim 3, wherein the expertise profile is based on a citation ratio computed as the number of citations to authors contained in publications associated with a conference divided by the number of publications associated with the conference.

10. The method of claim 9, wherein the expertise profile is also based on a publication impact determined by the quality of the conference with which the paper is associated, as well as an expert impact determined by the number of times the expert is cited and the quality of the citing publications.

11. A computer-implemented method comprising:

generating a set of nodes by extracting features from a dataset according to at least a first heuristic;

representing at least a first feature relationship using the nodes, a second feature relationship using a first link, and a third feature relationship using a second link, wherein each of said first and second links has an endpoint at one of the nodes;

assigning a weight for each link based on a second heuristic;

ranking the nodes based on the first and second heuristics; and

outputting a report including an indication of the ranking.

12. The method of claim 11, in which the first heuristic is an impact profile generated for each expert based on the number of links and their quality weighting associated with the expert.

13. The method of claim 11, in which the second heuristic is an expertise social network score.

14. The method of claim 12, wherein the first link represents a first relationship among publications and authors.

15. The method of claim 14, wherein the first link is a citation link for which each instance represents a citation of the expert by a publication or a citation by another publication of a publication associated with the expert.

16. The method of claim 15, wherein the second link is a co-author link for which each instance represents co-authorship of a publication by the expert.

17. The method of claim 16, wherein the third link is a co-citation link for which each instance represents citation by a publication of the expert along with other experts.

18. The method of claim 11, wherein the ranking is based on an expertise social profile.

19. The method of claim 18, wherein the ranking is based on an expert impact determined from both the number of publications citing the expert and the quality of the citing publications.

20. The method of claim 11, wherein the report includes a visual representation of a network formed from the nodes and links.

21. A system comprising:

a feature extractor configured to obtain information from a dataset;

an impact analyzer configured to analyze extracted feature information to produce an impact ranking;

a network builder configured to construct at least a first and a second network, wherein each network is a representation of a different set of relationships among dataset items; and

a network integrator and data analyzer configured to perform analysis using a combination of the at least first and second networks and the impact ranking based on at least one relationship determined to exist between items in the dataset according to heuristics.

22. The system of claim 21, wherein the first network is constructed to identify at least one expertise relationship and the second network is constructed to identify at least one social relationship.

23. The system of claim 21, wherein the network builder is further configured to analyze of the information represented by each of the first network and the second network.

24. The system of claim 23, wherein the network builder is further configured to perform the analysis separately over discrete periods of time and to output an indication of the network evolution with respect to the analysis results over time based on the results determined for each discrete time period.

25. The system of claim 22, wherein the at least one social relationship is collaboration.

26. The system of claim 21, wherein the network integrator and data analyzer is further configured to perform the analysis separately over discrete periods of time and to output an indication of the combined network evolution with respect to the analysis results over time based on the results determined for each discrete time period.

27. The system of claim 26, wherein the network integrator and data analyzer is further configured to identify evolutionary points.