Candidate visualization techniques for use with genetic algorithms

1. A processor-implemented method for generating a plurality of candidate visualizations, the method comprising:

collecting a plurality of expert data entered through interactions by a subject matter expert user with a graphical user interface of a training system based on a scenario-description, wherein the plurality of expert data comprises aspects and attributes of visualizations favored by the subject matter expert user when assessing a fitness of the visualizations; and wherein the plurality of expert data further comprises metric data, action data, and opinion data, and wherein the plurality of metric data comprises statistical measurements captured from within a visualization or metadata of fields used by the visualization including skewness of a field and kurtosis of a field, and wherein the action data comprises metadata capable of capturing aspects of actions within a visualization, and wherein the opinion data comprises free text opinions regarding the visualization provided by a subject matter expert and data regarding an expertise level of the subject matter expert;

generating at least one predictive model based on the plurality of collected expert data;

calculating a fitness score for each of a plurality of candidate visualizations by executing the at least one generated predictive model during an application of a plurality of genetic algorithms; and

generating a next generation of candidate visualizations using the plurality of genetic algorithms by mutating or cross-breeding candidate visualizations with a calculated fitness score that satisfies a preconfigured threshold value.

2. The method of claim 1 , wherein the training system collects the plurality of expert data through at least one of an expert selecting a preferred visualization within a plurality of candidate visualizations and an expert submitting an opinion related to the preferred visualization.

3. The method of claim 1 , wherein the training system includes at least one of a central data storage facility, a plurality of web-based deployment capabilities, and scalability to a corresponding number of experts using the training system.

4. The method of claim 1 , further comprising:

updating the training system based on the at least one generated predictive model.

5. The method of claim 1 , wherein generating the at least one predictive model comprises using boosted decision trees, text categorization, and neural networking.

6. The method of claim 1 , wherein each at least one generated predictive model may be used to derive an overall fitness evaluation score to rate a candidate visualization when executing the plurality of genetic algorithms.

7. A computer system for generating a plurality of candidate visualizations, the method comprising:

collecting a plurality of expert data entered through interactions by a subject matter expert user with a graphical user interface of a training system based on a scenario-description, wherein the plurality of expert data comprises aspects and attributes of visualizations favored by the subject matter expert user when assessing a fitness of the visualizations; and wherein the plurality of expert data further comprises metric data, action data, and opinion data, and wherein the plurality of metric data comprises statistical measurements captured from within a visualization or metadata of fields used by the visualization including skewness of a field and kurtosis of a field, and wherein the action data comprises metadata capable of capturing aspects of actions within a visualization, and wherein the opinion data comprises free text opinions regarding the visualization provided by a subject matter expert and data regarding an expertise level of the subject matter expert;

generating at least one predictive model based on the plurality of collected expert data;

calculating a fitness score for each of a plurality of candidate visualizations by executing the at least one generated predictive model during an application of a plurality of genetic algorithms; and

generating a next generation of candidate visualizations using the plurality of genetic algorithms by mutating or cross-breeding candidate visualizations with a calculated fitness score that satisfies a preconfigured threshold value.

8. The computer system of claim 7 , wherein the training system collects the plurality of expert data through at least one of an expert selecting a preferred visualization within a plurality of candidate visualizations and an expert submitting an opinion related to the preferred visualization.

9. The computer system of claim 7 , wherein the training system includes at least one of a central data storage facility, a plurality of web-based deployment capabilities, and scalability to a corresponding number of experts using the training system.

10. The computer system of claim 7 , further comprising:

updating the training system based on the at least one generated predictive model.

11. The computer system of claim 7 , wherein generating the at least one predictive model comprises using boosted decision trees, text categorization, and neural networking.

12. The computer system of claim 7 , wherein each at least one generated predictive model may be used to derive an overall fitness evaluation score to rate a candidate visualization when executing the plurality of genetic algorithms.

13. A non-transitory computer-readable storage medium having stored therein instructions which, when executed by a processor, cause the processor to perform a method for generating a plurality of candidate visualizations, the method comprising:

collecting a plurality of expert data entered through interactions by a subject matter expert user with a graphical user interface of a training system based on a scenario-description, wherein the plurality of expert data comprises aspects and attributes of visualizations favored by the subject matter expert user when assessing a fitness of the visualizations; and wherein the plurality of expert data further comprises metric data, action data, and opinion data, and wherein the plurality of metric data comprises statistical measurements captured from within a visualization or metadata of fields used by the visualization including skewness of a field and kurtosis of a field, and wherein the action data comprises metadata capable of capturing aspects of actions within a visualization, and wherein the opinion data comprises free text opinions regarding the visualization provided by a subject matter expert and data regarding an expertise level of the subject matter expert;

generating at least one predictive model based on the plurality of collected expert data;

calculating a fitness score for each of a plurality of candidate visualizations by executing the at least one generated predictive model during an application of a plurality of genetic algorithms; and

generating a next generation of candidate visualizations using the plurality of genetic algorithms by mutating or cross-breeding candidate visualizations with a calculated fitness score that satisfies a preconfigured threshold value.

14. The computer program product of claim 13 , wherein the training system collects the plurality of expert data through at least one of an expert selecting a preferred visualization within a plurality of candidate visualizations and an expert submitting an opinion related to the preferred visualization.

15. The computer program product of claim 13 , wherein the training system includes at least one of a central data storage facility, a plurality of web-based deployment capabilities, and scalability to a corresponding number of experts using the training system.

16. The computer program product of claim 13 , further comprising:

updating the training system based on the at least one generated predictive model.

17. The computer program product of claim 13 , wherein generating the at least one predictive model comprises using boosted decision trees, text categorization, and neural networking.