1. Chapter 11 Information Visualization

2. Outline Scientific Visualization (scivis)
Engineering, computational fluid mechanics, mathematics to medical and earth sciences
Data coming from numerical simulations and measurements of physical quantities
Information Visualization (infovis)
More abstract of data
Generic graph, trees to database tables, text and computer software
Present a succinct overview of inforvis methods and techniques

3. Outline 11.2 goal of infovis
11.3 similarities and differences between the scivis and infovis fields
11.4 visualization of the database table
11.5 visualization of relational data
11.6 visualization of multivariate data
11.7 visualization of text document

4. 11.2 What’s infovis?
To visualize is to “form a mental model or mental image of something” [Spence 07]
Broad definition: visualization applies to abstract quantities and relations in order to get insight in the data [Chi 02 ]
Inforvis application
a wider spectrum of data types than scivis applications: data that has no physical placement
Abstract data: computer file systems, databases, documents from archives, and stock exchange courses

5. 11.3 Infovis VS Scivis 11.3.1 Dataset
Figure Examples of (a) scivis and (b) infovis datasets.

6. 11.3 Infovis VS Scivis 11.3.2 Data Domain
The domain of a scivis dataset typically describes a compact region of sampled at several locations
Infovis:
no spatial information (sample points)
Don’t contain cells having the function of interpolation

7. 11.3 Infovis VS Scivis 11.3.3 Data Attributes
Infovis data values are of more types than numerical values
SciVis classification:
The kind of scale: nominal,ordinal, binary, discrete, and continuous
Qualitative, quantitative and categorical
Linear, planar, volumetric, temporal, multidimensional, tree, network, and workspace.
Values and Relations

8. 11.3 Infovis VS Scivis 11.3.3 Data Attributes
Table Attribute data types in infovis.

9. 11.3 Infovis VS Scivis 11.3.4 Interpolation
Infovis: inherently discrete
SciVis: originally continuous
Table Comparison of dataset notions in scivis and infovis.

10. 11.4 Table Visualization
Figure Textual visualization of a database table containing stock exchange data.

11. 11.4 Table Visualization
Figure Table visualization enhanced using multiple sorting, evolution icons, bar graphs, and same-value (date) row cues.

12. 11.4 Table Visualization
Focus and context
Figure The table lens technique allows us to create overviews of large tables as well as show context information.

13. 11.5 Visualization of Relations
A relation is an association between two or more items
The information is located in the fact that several data values associated in some way
Ubiquitous in many application domain
Trees, graphs, and venn diagrams

14. 11.5 Visualization of Relations 11.5.1 Tree Visualization
Two different methods for visualizing trees
Ball-and-stick drawing (the most widespread method)
treemaps

15. 11.5 Visualization of Relations 11.5.1 Tree Visualization
Figure File hierarchy of the FFmpeg software distribution visualized using a rooted tree.

16. 11.5 Visualization of Relations 11.5.1 Tree Visualization
Figure Radial-tree layout for the same file hierarchy as in Figure 11.5.

17. 11.5 Visualization of Relations 11.5.1 Tree Visualization
Figure 11.7 Bubble-tree layout for the same file hierarchy as in Figure 11.5.

18. 11.5 Visualization of Relations 11.5.1 Tree Visualization
Figure Cone-tree layout for the same file hierarchy as in Figure 11.5.

19. 11.5 Visualization of Relations 11.5.1 Tree Visualization
Two different methods for visualizing trees
Ball-and-stick drawing (the most widespread method)
Limitation: take a considerable amount of space
Treemaps
A different layout for tree structures that use virtually every pixel of display space to convey information
Every subtree is represented by a rectangle

20. 11.5 Visualization of Relations 11.5.1 Tree Visualization
Disadvantages:
Aspect ratio of the rectangles can become quite far from unity
The nonleaf node visibility problem
Addressed by a variant of the treemap layout (next slide)
Figure Treemap layout for the same file hierarchy as in Figure Colors indicate file types; rectangle areas indicate file sizes.

21. 11.5 Visualization of Relations 11.5.1 Tree Visualization
Figure Improved treemap visualization using squarified layout and shaded cushion rendering.

22. 11.5 Visualization of Relations 11.5.1 Tree Visualization
Figure (a) The tree structure is visualized with (b) a cushion treemap. The actual cushion surface is indicated by the bold black line in (b). The same color is used to indicate the same node in the ball-and-stick tree drawing, the treemap, and the cushion profiles.

23. 11.5 Visualization of Relations 11.5.1 Tree Visualization
Figure The Map of the Market [SmartMoney 07J rendered using a treemap.

24. 11.5 Visualization of Relations 11.5.2 Graph Visualization
Graphs are the most general type of relational data
Different methods:
Hierarchical graph visualization

25. 11.5 Visualization of Relations 11.5.2 Graph Visualization
Figure The evolution of the UNIX operating system, displayed as a hierarchical graph.

26. 11.5 Visualization of Relations 11.5.2 Graph Visualization
Figure The call graph of a program visualized using a hierarchical graph layout. Note the separation betwccn the main program and library subsystem.

27. 11.5 Visualization of Relations 11.5.2 Graph Visualization
Variation of hierarchical graph layout
Figure Containment and dependency relations in a software system, visualized using a hierarchical graph layout with orthogonal edge routing.

28. 11.5 Visualization of Relations 11.5.2 Graph Visualization
Figure The call graphs of two programs visualized in relation to their hierarchical layering. The layout used suggests that the left system is more modular than the right system.

29. 11.5 Visualization of Relations 11.5.2 Graph Visualization
Graphs are the most general type of relational data
Different methods:
Hierarchical graph visualization
Force directed layout

30. 11.5 Visualization of Relations 11.5.2 Graph Visualization
listing Force-directed graph layout algorithm.

31. 11.5 Visualization of Relations 11.5.2 Graph Visualization
Figure Call graph of a C++ program visualized using a force-directed layout. The node colors indicate the function types. The graph contains 314 nodes (functions) and 718 edges (calls).

32. 11.5 Visualization of Relations 11.5.2 Graph Visualization
Figure Inheritance relations in the VTK class library visualized using the GEM force-directed layout. Specialization subtrees arc indicated by blue outlines and labeled by the respective subtree root class.

33. 11.5 Visualization of Relations 11.5.2 Graph Visualization
Graphs are the most general type of relational data
Different methods:
Hierarchical graph visualization
Force directed layout
Multiple views

34. 11.5 Visualization of Relations 11.5.2 Graph Visualization
Figure Hierarchical and call relations in a software system visualized with a combination of tree and forcc- directed layouts. The bottom view shows theentire system hierarchy, where two subsystems of interest have been selected (rendered in red). The top-left view shows the call and hierarchy relations in the selected subsystems using a. force-directed layout. The top-right view shows a simplified view of the latter, where several call relations have been filtered out. The arrows between the images show the order of creating and examining the visualizations.

35. 11.5 Visualization of Relations 11.5.2 Graph Visualization
Graphs are the most general type of relational data
Different methods:
Hierarchical graph visualization
Force directed layout
Multiple views
Graph splatting
Multidimensional scaling

36. 11.5 Visualization of Relations 11.5.2 Graph Visualization
Figure Software dependency graph visualized with {a} force-directed layout and (b) graph splatting (b). The splatting density is scaled by the number of dependent modules. Warm colors in (b) emphasize high-level system modules. The nodes, positioned identically to the layout shown in (a), are depicted by white dots.

37. 11.6 Multivariate Data Visualization
A dataset is called multivariate
there are several variables, or attributes, per data point.
We are interested in
examining the correlation and distribution of the individual values of the various dimensions
the overall distances between the data points
Parallel coordinate plot

38. 11.6 Multivariate Data Visualization
Figure Parallel coordinate plot showing six attributes (miles-per-gallon, cylinders, horsepower, weight, acceleration, and manufacturing year) for about 400 cars. A selected car is shown in the image as a red polyline with the individual attribute values displayed as labels

39. 11.6 Multivariate Data Visualization
Figure Using brushing to select the low-acceleration cars. The selected cars are shown in red. An interesting outlier is highlighted further.

40. 11.6 Multivariate Data Visualization
Figure Enhancing parallel coordinates. The orientation of the axes whose labels are marked in red has been swapped as compared to Figure Histograms show the attribute value distribution over 10 equally sized ranges for each axis.

41. 11.7 Text Visualization Information contained in a text document:
Content: information contained in the text itself
Structure: characterize how the text is organized
Metadata: describes all types of information related to the text that are not contained in the text itself

42. 11.7 Text Visualization 11.7.1 Content-Based Visualization
Figure Visualization of an electronic (PDF) version of this book in the Adobe Acrobat system. Four design elements are emphasized. (a) The document's detailed content. (b) A page-level overview. (c) The document structure. (d) Annotation metadata.

43. 11.7 Text Visualization 11.7.2 Visualizing Program Code
Figure Visualization of C source code using the SeeSoft tool. Color shows the code age. Red depicts recently modified code, while blue shows code unchanged for a long time. The smaller window in front shows detail for a region in focus in the form of actual source code text.

44. 11.7 Text Visualization 11.7.2 Visualizing Program Code
Figure Visualization of C++ source code using shaded cushions. Color shows the occurrence of selected construct types. The cushion luminance profiles emphasize the syntactic nesting of structures.

45. 11.7 Text Visualization 11.7.3 Visualizing Software Evolution
Figure Visualization of the evolution of the VTK software project. Files are shown as horizontal pixel strips colored by file type. File strips are stacked on the vertical axis in the order they appear in the directories_ Yellow dots indicate the file modification events.

46. 11.7 Text Visualization 11.7.3 Visualizing Software Evolution
Figure Visualization of author contributions in the VTK software project. The file versions are colored by the author who modified them. File strips are stacked on the vertical axis in decreasing order of activity, with the most modified files shown at the top.

47. 11.8 Conclusion Inforvis is a rapidly growing field
Providing insight to users into complex data
Challenges
the task of making abstract data visible
The challenge of high dimensionality
A set of attributes including noninterpolable types, such as text and relations
The request for effective interaction mechanism with the information space
End users