Multi-variate, Time-varying, and Comparative Visualization with Contextual Cues

1. Jon Woodring and Han-Wei Shen The Ohio State University Multi-variate, Time-varying, and Comparative Visualization with Contextual Cues

2. Long title... so what is it? A visualization that compares multiple data volumes (variables, time-steps, and runs) by operating on them per point and combining them into one space Interactive Demo

3. Previous Work Past techniques include: Bajaj et al.: Hypervolume Visualization Cai and Sakas: Data Intermixing and Multi-Volume Rendering Chen and Tucker: Constructive Volume Geometry Woodring et al.: Chronovolumes and High-Dimensional Direct Rendering of Time-Varying Volumetric Data

4. How is it different? Uses modified Porter and Duff style set operators to compare volumes Set operators allow for visual query system Avoids high dimensional operations and projection Complete framework for set operations; simple to implement, pipeline, and data parallelize Volume shader framework that can compare volumes with an arbitrary number of operations and comparisons Additionally, we can “post-classify” by operating on data rather than operate on color volumes User interface and contextual visualization helps the user understand her data Allows the user to examine every step of the visualization to explore more effectively

5. Porter and Duff Review Describes how to combine images together Given two images A and B, with pre-multiplied alpha colors (Porter and Duff 1984)

6. Volume Set Operations Operators applied per volume point Rather than using an alpha value, a set weighting function is used that maps data points to weight values Weight determines how much and what portions of a volume you wish to show rather than using alpha Can be thought of as an interest level Weight ranges from 0 to 1 like alpha Where weight = alpha for every data point is a special case: Porter and Duff alpha compositing in 3D By introducing the weighting function, we separate the appearance of a volume from the set operations on a volume

7. Volume Set Operations A out B weights = 1 A A out B weights = alpha B

8. In and Out A A in B B in A A out B B out A B

9. Atop and Xor A A atop B B atop A A xor B B

10. Volume Set Operations Value based set operations Value based set operators allow users to compare volumes in data space rather than color space The transfer function is applied after operations Two types of value based set operators Data blending (requires the same data type) Binary decision (does not require the same data type)

11. Volume Set Operations

12. Numerical or Statistical Operations Since the framework allows for data operations, numerical or statistical data parallel operations can be added New data fields can be created during the visualization and exploration process Examples of operations: Scalars: Sum, Difference, Product Vectors: Magnitude, Dot product Statistical: Min, Max, Standard Deviation Matrix: Curvature, Matrix Multiply

13. User Interaction The user constructs her visualization from the bottom up She selects input data and chooses what operation to apply The data result appears in a visualization spreadsheet and as a node in a volume tree (graph representation of a volume shader)

14. Contextual Rendering By displaying the visualization spreadsheet and volume tree, we show the context of the visualization We define the context of our visualization as the inputs and sub-operations that compose the final visualization It allows the user to see how sub-operations are dependent on inputs It allows the user to have a better understanding of how the input data is filtered through operations It allows the user to change and fine tune their visualization

15. Contextual Rendering ? ?

16. Contextual Rendering

17. Context via Level-of-detail In level-of-detail contextual rendering, other operation nodes in the volume tree are displayed in the same space along with the final visualization (root of the volume tree) Lower detail includes lower opacity or NPR methods to display an indication or hint of the context data without obscuring the final result Context highlighting can also be done via volume motion

18. Context via Level-of-detail A out B A in B B out A

19. Context via Animation The level of detail method is not able to show every child or sub-operation in the volume tree; through animation, the user is able to see all nodes of interest over time Animating up or down a branch of the tree shows how one input is operated on Animating across a level of a tree shows all the inputs to one particular operation

20. Context via Animation

21. Implementation GLSL is used to implement the volume shader; new fragment programs are created on the fly as the user builds the volume tree Data is uploaded as 3D textures, and interleaved into the RGBA channels Only one texture is reserved for the transfer function and weighting function lookup Complexity of the volume tree is only limited to fragment program size limitations No loops are needed since fragment programs are data parallel For visualizations without lighting, only 2 to 3 texture lookups are needed per data field accessed per fragment

22. Conclusion Multi-variate, Time Varying, Comparative Visualization Exploration Set Operators Color Space Data Space Numerical or Statistical Operators User Interaction through Spreadsheet and Volume Tree Contextual Rendering Level -of-Detail and Animation Contextual Rendering

23. Thanks NSF ITR Grant ACI-0325934 NSF RI Grant CNS-0403342 DOE Early Career Principal Investigator Award DE-FG02-03ER25572 NSF Career Award CCF-0346883 Oak Ridge National Laboratory Contract 400045529 TSI Data: John M. Blondin (NCSU), Anthony Mezzacappa (ORNL), and Ross J. Toedte (ORNL) Vortex Data: Kwan-Liu Ma via NSF ITR Hurricane Isabel: NCAR and the U.S. National Science Foundation Anonymous Reviewers The audience for viewing my presentation Any Questions?