Chapter 11 Information Visualization

1. Chapter 11Information 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 Scivis11.3.1 Dataset Figure 11.1. Examples of (a) scivis and (b) infovis datasets.

6. 11.3 Infovis VS Scivis11.3.2 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 Scivis11.3.3 Data Attributes • infovis VS Scivis • 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 Scivis11.3.3 Data Attributes Table 11.1. Attribute data types in infovis.

9. 11.3 Infovis VS Scivis11.3.4 Interpolation Table 11.2. Comparison of dataset notions inscivis and infovis. • Infovis: inherently discrete • SciVis: originally continuous

10. 11.4 Table Visualization Figure 11.2. Textual visualization of a database table containing stock exchange data.

11. 11.4 Table Visualization Figure 11.3. Table visualization enhanced using multiple sorting, evolution icons, bar graphs, and same-value (date) row cues.

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

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 Relations11.5.1 Tree Visualization • Two different methods for visualizing trees • Ball-and-stick drawing (the most widespread method) • treemaps

15. 11.5 Visualization of Relations11.5.1 Tree Visualization Figure 11.5. File hierarchy of the FFmpeg software distribution visualized using a rooted tree.

16. 11.5 Visualization of Relations11.5.1 Tree Visualization Figure 11.6. Radial-tree layout for the same file hierarchy as in Figure 11.5.

17. 11.5 Visualization of Relations11.5.1 Tree Visualization Figure 11.7 Bubble-tree layout for the same file hierarchy as in Figure 11.5.

18. 11.5 Visualization of Relations11.5.1 Tree Visualization Figure 11.8. Cone-tree layout for the same file hierarchy as in Figure 11.5.

19. 11.5 Visualization of Relations11.5.1 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 Relations11.5.1 Tree Visualization Figure 11.9. Treemap layout for the same file hierarchy as in Figure 11.5. Colors indicate file types; rectangle areas indicate file sizes. 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)

21. 11.5 Visualization of Relations11.5.1 Tree Visualization Figure 11.10. Improved treemap visualization using squarified layout and shaded cushion rendering.

22. 11.5 Visualization of Relations11.5.1 Tree Visualization Figure 11.11. (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 Relations11.5.1 Tree Visualization Figure 11.12. The Map of the Market [SmartMoney 07J rendered using a treemap.

24. 11.5 Visualization of Relations11.5.2 Graph Visualization • Graphs are the most general type of relational data • Different methods: • Hierarchical graph visualization

25. 11.5 Visualization of Relations11.5.2 Graph Visualization Figure 11.13. The evolution of the UNIX operating system, displayed as a hierarchical graph.

26. 11.5 Visualization of Relations11.5.2 Graph Visualization Figure 11.14. 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 Relations11.5.2 Graph Visualization Variation of hierarchical graph layout Figure 11.15. Containment and dependency relations in a software system, visualized using a hierarchical graph layout with orthogonal edge routing.

28. 11.5 Visualization of Relations11.5.2 Graph Visualization Figure 11.16. 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 Relations11.5.2 Graph Visualization • Graphs are the most general type of relational data • Different methods: • Hierarchical graph visualization • Force directed layout

30. 11.5 Visualization of Relations11.5.2 Graph Visualization listing 11.1. Force-directed graph layout algorithm.

31. 11.5 Visualization of Relations11.5.2 Graph Visualization Figure 11.17. 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 Relations11.5.2 Graph Visualization Figure 11.18. 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 Relations11.5.2 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 Relations11.5.2 Graph Visualization Figure 11.19. 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 Relations11.5.2 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 Relations11.5.2 Graph Visualization Figure 11.20. 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 11.21. 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 11.22. 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 11.23. Enhancing parallel coordinates. The orientation of the axes whose labels are marked in red has been swapped as compared to Figure 11.22. 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 Visualization11.7.1 Content-Based Visualization Figure 11.24. 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 Visualization11.7.2 Visualizing Program Code Figure 11.25. 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 Visualization11.7.2 Visualizing Program Code Figure 11.26. 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 Visualization11.7.3 Visualizing Software Evolution Figure 11.27. 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 Visualization11.7.3 Visualizing Software Evolution Figure 11.28. 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