1. Drag and Track: A Direct Manipulation Interface for Contextualizing Data Instances within a Continuous Parameter Space
Daniel Orban, University of Minnesota
Daniel F. Keefe, University of Minnesota
Ayan Biswas, Los Alamos National Laboratory
James Ahrens, Los Alamos National Laboratory
David Rogers, Los Alamos National Laboratory

2. Drag and Track: A Direct Manipulation Interface for Contextualizing Data Instances within a Continuous Parameter Space
Daniel Orban, University of Minnesota
Daniel F. Keefe, University of Minnesota
Ayan Biswas, Los Alamos National Laboratory
James Ahrens, Los Alamos National Laboratory
David Rogers, Los Alamos National Laboratory

3. What would you do with an aluminum alloy?
Pressure

4. What if you were a material scientist?
Pressure

5. A material scientist would want to shoot it.
Pressure

6. Velocity Pressure Temperature What if…? Credit: JPL-Calteck/NASA
Credit: NASA's Goddard Space Flight Center/SDO
Photo: Jerry Lara /San Antonio Express-News

7. Running a shock physics experiment.

8. The shock physics input output model.
Velocimetry Profile

9. Run the experiment enough times with different inputs and you get an ensemble.

10. Run the experiment enough times with different inputs and you get an ensemble.
Goals:
Understand the high-dimensional parameter space.
Explore the gaps in the ensemble.
Understand the input from the output.

11. Roadmap Motivation Related Work Drag and Track
Results, Evaluation, and Conclusions

12. To assist with parameter space analysis we use a direct manipulation interface within a dimensionally reduced space.
Visual Parameter Space Analysis
Direct Manipulation within Dimensionality Reduction
Sedlmair et al. 2014, IEEE VIS
Cavallo and Demiralp,
2018, ACM CHI
Endert et al. 2011, IEEE VIS
Input and Output Spaces
Navigational Strategies
Local-to-Global
Global-to-Local
Prediction
Uncertainty / Sensitivity
Understanding dimensionally reduced views
Spatial relationships have meaning
Underlying mechanisms are hidden from the user

13. Like related work in SciVIS our direct manipulation works in both forward and inverse directions.
Forward Design
Inverse Design
Design by Dragging, Coffey, et al. 2013, IEEE VIS

14. We know it is critical to visualize prediction uncertainty together with the ensemble.
Torsney-Weir et al. 2011, IEEE VIS
Berger et al. 2011, Computer Graphics Forum

15. Roadmap Motivation Related Work Drag and Track
Results, Evaluation, and Conclusions

16. Key Idea: Directly manipulate Virtual Data Instances (VDI) to interactively explore a parameter space.
Virtual Data Instance

17. Key Idea: Directly manipulate Virtual Data Instances (VDI) to interactively explore a parameter space.
Virtual Data Instance

18. Key Idea: Directly manipulate Virtual Data Instances (VDI) to interactively explore a parameter space.
Virtual Data Instance
Interactive Callout

19. Key Idea: Directly manipulate Virtual Data Instances (VDI) to interactively explore a parameter space.
Virtual Data Instance
Interactive Callout

20. Key Idea: Directly manipulate Virtual Data Instances (VDI) to interactively explore a parameter space.
Virtual Data Instance
Interactive Callout
Nearest Neighbor
Highlights

21. Drag and Track Interface
Parallel Coordinates Plot for Filtering in Either Space
Input Space
Output Space

22. Drag and Track Interface
Parallel Coordinates Plot for Filtering in Either Space
Input Space
Output Space

23. Example Task 1: Explore the parameter space through annotation.

24. Example Task 2: Track parameter changes by dragging the elastic plateau.

25. Example Task 3: Study the sensitivity by moving VDIs across the input and output spaces.

26. What is a Virtual Data Instance?
Parameters
Name: Output 137
Value: 3.14
Weight: 0.75
Data Instance P1 P2 P3
... Pn
Name: Pressure
Value: 2.718
Name: PCA 1
Value: 3.14
Index: 27
Weight: 0.125
Virtual Data Instance
Query Set: P2 P7
...
P23
Predicted Data Instance: P1 P2 P3
... Pn
Nearest Neighbors (index): N1 N2
... Nk

27. Roadmap Motivation Related Work Drag and Track
Results, Evaluation, and Conclusions

28. Input parameter A qualitatively correlates with the elastic plateau output feature.

29. Domain Expert Evaluation
Domain Experts:
2 Shock Physics Experimentalists
1 Fluid Simulation Expert
Time:
Each user evaluated Drag and Track for minutes.
Methodology:
The first 10 minutes involved walking the user through the application.
The last minutes was a talk out loud discussion.
A series of questions were asked related to each of the features in Drag and Track and whether they contributed to the following goals:
Understanding the input and output space
Uncertainty and sensitivity
Directly manipulating the output to gain insight about the input.

30. Direct manipulation enables sensitivity analysis by changing one value and observing the others.
I think certainly, there is alot there to look at with sensitivity. [I can] move around one input value, all at the same point,... to measure sensitivity of different parameters...
As we drag the elastic plateau, [we] would we be able to see range in specific parameters. Was there one particular parameter that moved highly through the path?

31. DR views are still abstract, but the nearest neighbor spread across spaces intuitively shows model uncertainty.
You can really tell you the nearest neighbors… It is a little abstract understanding where I am in the PCA space and seeing how that is a real parameter.
In terms of how [the input and output space] would be related, it does give me some information about jumping between the two spaces…
[The uncertainty] distribution is covered by nearest nearest neighbors.

32. Tracks show model correlation between the input and output spaces.
Input Path
(large curve)
Output Path
(tight curve)
I really like drag lines that show up… the clean line in the parameter space shows how it has a tight curve in one place and a large [curve] in the other… That [shows] how well the model correlates..

33. Overall Feedback Great for quickly exploring new data sets.
Opportunity to match experiment output curves with inverse direct manipulation.

34. Summary and Conclusions
Drag & Track uses the concept of "virtual data instances" to enable forward and inverse direct manipulation within dimensionally reduced visualizations of high-dimensional parameter spaces.
Several extensions are also introduced, including interactive callouts, track lines, and nearest-neighbor highlighting to convey uncertainty.
Our initial application and evaluations with domain scientists (along with experiments on a synthetic dataset described in the paper) suggest Drag & Track can be useful for identifying trends and performing visual parameter sensitivity analysis.
We believe the visuals and integrated interaction techniques show promise for helping users contextualize ensembles in continuous parameter spaces across a variety of other domains as well.

35. Thank you! Acknowledgements Shock Physics Feedback
David Walters
Richard Sandberg
Cindy Bolme
Color Map
Francesca Samsel
Software is available and open source!
Cinema Quest:
This document was published under LA-UR

37. Key Idea: Directly manipulate Virtual Data Instances (VDI) to interactively explore a parameter space in context.

38. Drag and Track users can both manipulate VDIs and annotate spaces

39. Evaluation Tasks

40. Evaluation Tasks

41. Evaluation Tasks

42. Drag and Track - Contextualizing Data Instances

43. Velocity Pressure Temperature What if…? 8 km/sec
Credit: JPL-Calteck/NASA
What if…?
Velocity
Pressure
Temperature
8 km/sec
Credit: NASA's Goddard Space Flight Center/SDO
Photo: Jerry Lara /San Antonio Express-News

44. Shock Physics Light Gas Gun Typically hydrogen
Projectile speeds up to 8km / sec (¾ the velocity needed to escape the Earth’s gravity)
100 to 200 shots per year