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

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

What would you do with an aluminum alloy? Pressure

What if you were a material scientist? Pressure

A material scientist would want to shoot it. Pressure

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

Running a shock physics experiment.

The shock physics input output model. Velocimetry Profile

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

Example Task 1: Explore the parameter space through annotation.

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

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

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

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

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

Domain Expert Evaluation Domain Experts: 2 Shock Physics Experimentalists 1 Fluid Simulation Expert Time: Each user evaluated Drag and Track for 30-45 minutes. Methodology: The first 10 minutes involved walking the user through the application. The last 20-35 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.

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?

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.

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..

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

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.

Thank you! Acknowledgements Shock Physics Feedback David Walters Richard Sandberg Cindy Bolme Color Map Francesca Samsel Software is available and open source! Cinema Quest: https://github.com/cinemascience/cinema_quest This document was published under LA-UR-18-29162

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

Drag and Track users can both manipulate VDIs and annotate spaces

Evaluation Tasks

Evaluation Tasks

Evaluation Tasks

Drag and Track - Contextualizing Data Instances

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

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 https://www.researchgate.net/figure/Schematic-of-two-stage-light-gas-gun-Gas-gun-used-in-these-experiments-is-19-m-long_fig2_235478628