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Visualization for Science

Visualization for Science. Bill Howe. Topics. Motivation Color SciVis vs. InfoVis Core SciVis Techniques Vis + DB Open Problems. Check Assumptions: Why Visualize?. Problem: How do you apprehend 100k tuples? …when your short-term memory is 7-10 items. Solution:

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Visualization for Science

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  1. Visualization for Science Bill Howe

  2. Topics • Motivation • Color • SciVis vs. InfoVis • Core SciVis Techniques • Vis + DB • Open Problems

  3. Check Assumptions: Why Visualize? • Problem: • How do you apprehend 100k tuples? • …when your short-term memory is 7-10 items. • Solution: • Harness the visual acuity of the eye to convey enormous amounts of information very quickly • Use color, space, depth, icons, patterns to increase information bandwidth • Caveat: • Easy to misrepresent the data with so many visual dimensions available

  4. Anscombe’s Quartet

  5. Anscombe’s Quartet (2) • mean of the x values = 9.0 • mean of the y values = 7.5 • equation of the least-squared regression line: y = 3 + 0.5x • sums of squared errors (about the mean) = 110.0 • regression sums of squared errors (variance accounted for by x) = 27.5 • residual sums of squared errors (about the regression line) = 13.75 • correlation coefficient = 0.82 • coefficient of determination = 0.67

  6. Anscombe’s Quartet (3)

  7. John Snow

  8. Benefits of Visualization • More precise and revealing • Anscombe’s Quartet • Data density: numbers / cm^2 • Galaxy Map • Human eye is a very efficient pattern detector

  9. Topics • Motivation • Color • SciVis vs. InfoVis • Core Techniques • Vis + DB • Open Problems

  10. Color Matters Lloyd Treinish, IBM Research, http://www.research.ibm.com/people/l/lloydt/

  11. Color: RGB • Carries a natural biological interpretation • as well as a techonolgical interpretation

  12. RGB Justified

  13. HSV • More intuitive model for humans • Difficult to compute the additive RGB values “in your head”

  14. Helps define a curve through the color space Common shapes: Linear S-Curve Common paths: “Rainbow” Color Map Tools

  15. Color Matters (2) Lloyd Treinish, IBM Research, http://www.research.ibm.com/people/l/lloydt/

  16. PRAVDA • Perceptual Rule-based Architecture for Visualizing Data Accurately • Guides color map selection based on human perception • Specifically, choices pruned using • spatial frequency • data type • (ordinal, interval, ratio) • user-selected visualization goal • (isomorphic, segmentation, highlighting)

  17. PRAVDA

  18. Topics • Motivation • Color • SciVis vs. InfoVis • Core SciVis Techniques • Vis + DB • Open Problems

  19. Scientific Visualization

  20. Information Visualization

  21. InfoVis vs. SciVis SciVis: Scientific & physically based InfoVis: Abstract [Card, Mackinlay, & Shneiderman 1999] SciVis: Spatialization given InfoVis: Spatialization chosen [Munzner 2003] Melanie Tory, Visualization 2003, used with permission

  22. Problems with these categories • SciVis or InfoVis? • Scientific, but not physically based • Bioinformatics • Mathematics: e.g., f(i, j, k, w) = i2 + j2 + k2 + w2 • Physically based, but not necessarily scientific • Air traffic control systems • Maps Melanie Tory, Visualization 2003, used with permission

  23. Continuous Data Discrete Data 1st Attempt: Continuous & Discrete Data Parallel Coordinates Direct Volume Rendering [Hauser et al.,Vis 2000] [Fua et al., Vis 1999] Isosurfaces Glyphs Scatter Plots Line Integral Convolution [http://www.axon.com/gn_Acuity.html] Node-link Diagrams [Cabral & Leedom,SIGGRAPH 1993] Streamlines [Lamping et al., CHI 1995] [Verma et al.,Vis 2000] Melanie Tory, Visualization 2003, used with permission

  24. Problems with 1st Attempt BUT… • The same data can be visualized discretely orcontinuously • So, our interpretation of data is more important than characteristics of data [http://www.tvweather.com] [http://www.wunderground.com] Melanie Tory, Visualization 2003, used with permission

  25. [Simeon Potts] [http://www.chem.swin.edu.au/modules/mod2/formats.html] 2nd Attempt: Model not Data Continuous Model Discrete Model Melanie Tory, Visualization 2003, used with permission

  26. 3rd Attempt: What about spatialization? • Visualization can be categorized according to whether the spatial layout is given or chosen [Munzner 2003] • We extend this idea to a continuum: Given Decreasing constraints Chosen [http://www.graphviz.org] [Llyod Treinish] [Heilmann et al., InfoVis 2004] Melanie Tory, Visualization 2003, used with permission

  27. Ex: Biochemical Pathway Visualization source: Betsy Skovran

  28. Ex: Biochemical Pathway Visualization source: Lauro Lins and Claudio Silva, SCI, Utah

  29. Tangible Benefits • Reproducible Science • Visualization by Analogy • Reduce “Time to Insight”

  30. Topics • Motivation • Color • SciVis vs. InfoVis • Core SciVis Techniques • Vis + DB • Open Problems

  31. Visualization Techniques • Scalars • Glyphs • Color • Slices • Isosurface • Vectors • Barbs • Streamlines • Volume Rendering

  32. Structured Grids • Regular topology • Potentially irregular geometry • Fast, but difficult to construct for complex domains

  33. Unstructured Grids • Irregular Topology • Irregular Geometry • Easier to fit to complex domains, • but algorithms more complicated and slower

  34. Cutting Planes

  35. Isosurfaces

  36. Isosurfaces

  37. Isosurfaces (Isolines) • For each cell, mark nodes as above or below the isovalue • Problem: • Desired isosurface intersects cells in many different ways • Observation: • There is only a small number of configurations possible • …when considering algebraic equivalences

  38. Isosurfaces (Isolines) • 2D Case

  39. Isosurfaces • 3D case

  40. Isosurfaces (done) • Marching Cubes: • Same 16 cases work for larger cubes consisting of many cells • Suggests an Out-of-core algorithm that works on a block of cells at a time

  41. Streamlines

  42. Streamlines

  43. Streamlines • Algorithm • From a seed point, cast a ray in the direction of the vector field, repeat • Amounts to integrating the vector field • Design challenges • Evenly spaced streamlines • Discontinuities and boundaries

  44. Volume Rendering

  45. “Full” Volume Rendering

  46. Topics • Motivation • Color • SciVis vs. InfoVis • Core SciVis Techniques • Vis + DB • Open Problems

  47. Converging Requirements Vis DB

  48. Why Vis Needs DB “Transferring the whole data generated … to a storage device or a visualization machine could become a serious bottleneck, because I/O would take most of the … time. A more feasible approach is to reduce and preparethe data in situ for subsequent visualization and data analysis tasks.” -- SciDAC Review Current Research Topics in Vis: • “Query-driven Visualization” • “In Situ Visualization” • “Remote Visualization”

  49. Why DB Needs Vis

  50. Why DB Needs Vis (2) “What does the salt wedge look like?”

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