Temporal visualization algorithm for recognizing and optimizing organizational structure

• US 20060075335A1
• Filed: 09/29/2005
• Published: 04/06/2006
• Est. Priority Date: 10/01/2004
• Status: Abandoned Application

• Current Assignee: iQuest Global Consulting, LLC
• Original Assignee: Tekflo Limited
• Inventors: Gloor, Peter A.
• Application Number:
  US11/238,252
• Publication Number:
  US 20060075335A1
• Time in Patent Office: Days
• US Class Current: 715/273
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs
• CPC Class Codes: H04L 43/045 for graphical visualisation of monitoring data
• CPC Class Codes: H04L 43/14 using software, i.e. software packages network security related monitoring H04L63/1408

Abstract

A system is provided that takes as input the interrelationships which are observed between identified resources, and automatically generates interactive movies that depict a visualization of the of the interaction patterns among the identified resources. Each resource is represented as a dot. A line between two dots indicates a relationship. The closer the two dots are placed together, the more intensive is their relationship, that is, the more commonality or interaction those resources share. Further, the most active resources, namely the resources that have the most relational links or lines extending therefrom, are placed in the center of the network. Once the visualization movie has been built, a user can search for groupings of related resources by simply searching for and identifying the various clusters within the network.

Claims

1. A method for analyzing and visually displaying patterns of interrelationships between a plurality of selected resources, the method comprising the steps of:

collecting data related to interrelationships between each of the resources within said plurality of resources over a period of time;

analyzing said data using an algorithm to generate an output; and

generating a temporal visualization based on said output.

2. The method of claim 1, wherein said resources are selected from the group consisting of:

people, equipment, documents, discrete elements of data and email communications.

3. The method of claim 1, wherein said algorithm utilizes a plurality of points in time to generate a series of outputs, wherein said each output in said series of outputs is displayed sequentially to produce said temporal visualization.

4. The method in claim 3, wherein said algorithm compares said plurality of temporal visualizations to identify subgroups of highly interrelated resources within said plurality of resources.

5. The method in claim 4, wherein said algorithm calculates a value that provides and indication to a user that a subgroup of highly interrelated resources within said plurality of resources has been formed.

6. The method in claim 5, wherein said calculated value is group betweeness centrality.

7. The method in claim 6, wherein said group betweeness centrality value is calculated as a function of time and displayed in a temporal graph.

8. The method in claim 7, wherein said temporal graph is represented as a three-dimensional surface.

9. The method of claim 3, wherein said series of outputs are incrementally sampled to generate key frames, wherein only said key frames are displayed sequentially to produce said temporal visualization.

10. The method of claim 9, wherein said algorithm performs a calculation to smooth the visual transition between each of said key frames before sequentially displaying said key frames.

11. The method of claim 1, wherein said step of collecting data related to interrelationships between each of the resources within said plurality of resources over a period of time further comprises:

selecting an observation window having a duration that is less than said period of time;

collecting a first set of data related to interrelationships between each of said resources during said observation window;

storing said first set of data;

advancing said observation window incrementally within said period of time;

collecting a subsequent set of data related to interrelationships between each of said resources during said advanced observation window; and

storing said subsequent set of data.

12. The method of claim 11, wherein said data collection process is repeated until said observation window has been advanced to the end of said time period.

13. The method of claim 12, wherein each of said subsequent sets of data partially overlap at least one of an earlier collected set of data.

14. The method of claim 12, wherein each of said subsequent sets of data includes the information collected in each of said earlier collected sets of data.

15. A system for visually identifying and displaying correlations between selected resources:

a visual display means;

a graphic representation of each of said selected resources arranged on said visual display means;

a graphic representation of the relative interrelatedness between each of said selected resources as a function of passing time; and

an algorithm that monitors changes in the relative interrelatedness between each of said selected resources as a function of passing time to determine discrete times wherein the relative interrelatedness between some of said selected resources is at a particularly high level.

16. The system of claim 15, wherein said graphic representation further comprises:

an array of dots, wherein each of said dots depicts each of said selected resources; and

an array of lines, each of said lines extending between two of said dots within said array of dots, wherein each of said lines represents an interrelationship between said two dots.

17. The system of claim 16, wherein said algorithm varies the positioning of said dots within said array based on the relative interrelatedness of each of said resources corresponding to said dots.

18. The system of claim 17, wherein the dots representing closely related resources are positioned in dense clusters relative to one another and the dots representing peripherally related resources are positioned at a greater distance relative to the more closely related resources.

19. The system of claim 18, said algorithm comprising the following steps:

monitor interrelatedness of said selected resources over time to identify dense clusters;

monitor the number and density of lines extending between each of said resources and periodically calculate a constant that represents the relative interrelatedness between all of the resources at that point in time;

identify points in time wherein said constant abruptly changes;

examine dense resource clusters during the points in time wherein the constant abruptly changes to locate resource clusters that are highly interrelated and highly correlated;

determine and rank the relative interrelatedness of each of the resources within said highly related resource clusters.

20. The system of claim 19, wherein said algorithm utilizes a plurality of points in time to generate a series of graphic representations, wherein said each graphic representation in said series of graphic representations is displayed sequentially to produce said temporal visualization.

21. The system of claim 20, wherein said series of graphic representations are incrementally sampled to generate key frames, wherein only said key frames are displayed sequentially to produce said temporal visualization.

22. The method of claim 21, wherein said algorithm performs a calculation to smooth the visual transition between each of said key frames before sequentially displaying said key frames.

23. The system of claim 15, wherein said step of monitoring changes in the relative interrelatedness between each of said selected resources over a period of time further comprises:

selecting an observation window having a duration that is less than said period of time;

collecting a first set of data related to interrelationships between each of said resources during said observation window;

storing said first set of data;

advancing said observation window incrementally within said period of time;

collecting a subsequent set of data related to interrelationships between each of said resources during said advanced observation window; and

storing said subsequent set of data.

24. The system of claim 23, wherein said data collection process is repeated until said observation window has been advanced to the end of said time period.

25. The system of claim 24, wherein each of said subsequent sets of data partially overlap at least one of an earlier collected set-of data.

26. The system of claim 24, wherein each of said subsequent sets of data includes the information collected in each of said earlier collected sets of data.