1. CPT-S 415 Big Data
Yinghui Wu
EME B45

2. CPT_S 415 Big Data Data Visualization and Navigation
(Information Visualization)
Information Visualization
Graph visualization
Graph drawing and graph visualization
Graph layout
courtesy from Ivan Herman

3. “Close the loop” Data quality -> knowledge quality
(potential new area)
Data interpretation
Visual analytics
Graph visualization

5. Information Visualization5

6. Linking data to human
Collecting information is no longer a problem, but extracting value from information collections has become progressively more difficult.
Visualization links the human eye and computer, helping to identify patterns and to extract insights from large amounts of information
Visualization technology shows considerable promise from increasing the value of large-scales collections of information
Visualization can be classified as scientific visualization, software visualization, and information visualization

7. Visualization Classification
Scientific Visualization helps understanding physical phenomena in data (Nielson, 1991)
Mathematical model plays an essential role
Isosurfaces, volume rendering, and glyphs are commonly used techniques
Isosurfaces depict the distribution of certain attributes
Volume rendering allows views to see the entire volume of 3-D data in a single image (Nielson, 1991)
Glyphs provides a way to display multiple attributes through combinations of various visual cues (Chernoff, 1973)

8. Visualization Classification
Software Visualization helps people understand and use computer software effectively (Stasko et al. 1998)
Program visualization helps programmers manage complex software (Baecker & Price, 1998)
Visualizing the source code (Baecer & Marcus, 1990) data structure, and the changes made to the software (Erick et al., 1992)
Algorithm animation is used to motivate and support the learning of computational algorithms

9. What is Information Visualization?
Information visualization helps users identify patterns, correlations, or clusters
Structured information
Graphical representation to reveal patterns
Integration with various data mining techniques (Thealing et al., 2002; Johnston, 2002)
Unstructured Information
Need to identify variables and construct visualizable structures
The depiction of information using spatial/graphical representations, to facilitate comparison, pattern recognition, change detection, and other cognitive skills by making use of the visual system.

10. Information Visualization
Problem:
HUGE Datasets: How to understand them?
Solution
Take better advantage of human perceptual system
Convert information into a graphical representation.
Issues
How to convert abstract information into graphical form?
Do visualizations do a better job than other methods?

11. Goals of Information Visualization
Make large datasets coherent
(Present huge amounts of information compactly)
Present information from various viewpoints
Present information at several levels of detail
(from overviews to fine structure)
Support visual comparisons
Tell stories about the data

12. “Sci vis” versus “Info vis”
Scientific visualization: specifically concerned with data that has a well-defined representation in 2D or 3D space (e.g., from simulation mesh or scanner).
*Adapted from The ParaView Tutorial, Moreland 13

13. Information visualization
Information visualization: concerned with data that does not have a well-defined representation in 2D or 3D space (i.e., “abstract data”). 14

14. Data Attributes
Data attributes: Infovis has more data types than numerical values
Data Type
Attribute Domain
Operations
Examples
nominal
Unordered set
Comparison
(=)
Text, references, syntax elements
ordinal
Ordered set
Ordering
(=, <, >)
Ratings (e.g., bad, average, good)
discrete
Integer
Integer arithmetic
Line of code
continuous
real
Real arithmetic
Code metrics 15

15. Info Viz vs Sic Viz Scivis Infovis Data Domain spatial, compact
non-spatial, abstract
Attribute Type
numerical
any data type
Data Points
Samples over the domain
Tuples of attributes
Cells
Support interpolation
Describe relations
Interpolation
Piecewise continuous
Can be inexistent

16. Information representation in InfoViz17

17. Information Representation
Shneiderman (1996) proposed seven types of representation methods:
1-D, 2-D, 3-D
Multidimensional
Tree
Network
Temporal approaches

18. 1-D
TileBars (Hearst, 1995)

19. 2-D & 3-D
2D: To represent information as two-dimensional visual objects
Visualization systems based on self-organizing map (SOM) (Kohonen, 1995)
To help users deal with the large number of categories created for the mass textual data
3D: To represent information as three-dimensional visual objects
WebBook system folds web pages into three-dimensional books (Card et al., 1996)
3-D version of a tree or network
3-D hyperbolic tree to visualize large-scale hierarchical relationships (Munzner 2000)

20. Multidimensional
To represent information as multidimensional objects and projects them into a three-dimensional or a two-dimensional space
Dimensionality reduction algorithm will be used
Multidimensional scaling (MDS)
Hierarchical clustering
K-means algorithms
Principle components analysis
Examples
SPIRE system (Wise et al. 1995)
VxInsight System (Boyack et al. 2002)
Glyph representation has been used in various social visualization techniques (Donath, 2002) to describe human behavior during computer-mediated communication (CMC)

21. Table Visualization Simple list; does not support analysis, or insight22

22. Table Visualization Aided by Sorting, Bar Graph, Evolution Icons
Dense Pixel Display:
Bar Graph,
Table Lens 23

23. To represent hierarchical relationship
Tree
To represent hierarchical relationship
Challenge: nodes grows exponentially
Different layout algorithms have been applied
Examples
Tree-Map allocates space according to attributes of nodes (Johnson & Shneiderman 1991)
Cone Tree system uses 3-D visual structure to pack more nodes on the screen (Robertson et al., 1991)
Hyperbolic Tree projects subtrees on a hyperbolic plane and puts the plane (Lamping et al., 1995)

24. Tree Visualization
The TreeMap (Johnson & Shneiderman ‘91) Idea: Show a hierarchy as a 2D layout
Fill up the space with rectangles representing objects
Size on screen indicates relative size of underlying objects.
Treemap method: visualize the tree structure that use virtually every pixel of the display space to convey information
Every subtree is represented by a rectangle, that is partitioned into smaller rectangles with correspond to its children.
The position of the slicing lines determines the relative sizes of the child rectangles.
For every child, repeat the slicing recursively, swapping the slicing direction from vertical to horizontal or conversely 25

25. Tree Visualization: Examples
Treemap 26

26. Tree Visualization Rooted-Tree Layout Radial-Tree Layout
Ball-and-stick visualization: use the position and appearance of the glyphs
Rooted-Tree Layout
Radial-Tree Layout
3D Cone-Tree Layout 27

27. Graphs and Networks
To represent complex relationships that a simple tree structure is insufficient to represent
Citation among academic papers( C. Chen & Paul 2001; Mackinlay et al., 1995)
Documents linked by the internet (Andrews, 1995)
Spring-embedder model (Eades, 1984) along with its variants ( Davidson & Harel, 1996;l Fruchterman & Reingold, 1991) have become the most popular drawing algorithms.

28. Examples
Network visualization (vizster) 29

29. To represent information based on temporal order
Location and animation are commonly used visual variables to reveal the temporal aspect of information
Examples
Perspective Wall lists objects along the x-axis based on time sequence and presents attriibutes along the y-axis (Robertson et al., 1993)
In VxInsight system (Boyack et al., 2002), the landscape changes as the time changes.

30. Examples Geo data mapping Demo Cyber Attacks 31

31. Additional Examples http://map.norsecorp.com/v1/
NY Times words, words, numbers
Visual Complexity (from book by Manuel Lima)
50 examples (from June 2009, somewhat dated)
D3 Gallery 32

32. Visualization components
User-Interface Interaction: Color, Size, Texture, Proximity, Annotation, Interactivity
Immediate interaction not only allows direct manipulation of the visual objects displayed but also allows users to select what to be displayed (Card et al., 1999)
Shneiderman (1996) summarizes six types of interface functionality: Overview, Zoom, Filtering, Details on demand, Relate, history
Information Analytics
Indexing
Extract the semantics of information
Analysis
Clustering, classification 33

33. Visualization pipeline
Acquire -> Parse -> Filter -> Mine -> Represent -> Refine -> Interact
Represent
Parse
Interact
Acquire
Refine
Filter/Mine

34. Visualization software
Host language (C/C++/Java/Python) plus OpenGL
Stat/math package with graphics R
MATLAB
Special-purpose info viz software
Earth mapping, biological network visualization, etc.
Browser-enabled graphics/info viz packages
Google Charts
Processing / Processing.js D3
Java + Flash (becoming rarer) 35

35. Graph Drawing and Graph Visualization36

36. Information Visualization vs. Graph Drawing
Old topic, many books, etc.
May have other goals than visualization
E.g. VLSI design
Graph Visualization
Size key issue
Usability requires nodes to be discernable
Navigation considered

37. Graph Visualization
Hierarchical graph of the evolution of the UNIX operating system 38

38. Graph Visualization The Call Graph
Three concentric rings show containment
Files
Classes
Methods
The curved lines indicate function calls 39

39. Graph visualization
Circle chart 40

40. When is Graph Visualization Applicable?
Ask the question: is there an inherent relation among the data elements to be visualized?
If YES – then the data can be represented by nodes of a graph, with edges representing the relations.
If NO – then the data elements are “unstructured” and goal is to use visualization to analyze and discover relationships among data.
Source: Herman, Graph Visualization and Navigation in Information Visualization: a Survey

41. Traditional Graph Drawing
Optimization based on a set of criteria (mathematical aesthetics)
Minimize edge crossings
Minimize area
Maximize smallest angle
Maximize symmetry
Do all at once is hard.
Often unsuitable for interactive visualization
Many optimizations are NP-Hard
Approximation algorithms very complex
Precompute layout, or compute once at the beginning of an application then support interaction
Slide adapted from Jeff Heer

42. Traditional Graph Drawing
poly-line graphs (includes bends)
orthogonal drawing
planar, straight-line drawing
upward drawing of DAGs

43. Layout Approaches Tree-ify the graph - then use tree layout
Hierarchical graph layout
Radial graph layout
Optimization-based techniques
Includes spring-embedding / force-directed layout
Adjacency matrices
Structurally-independent layout
On-demand revealing of subgraphs
Distortion-based views
Hyperbolic browser
(this list is not meant to be exhaustive)

44. Tree-based graph layout
Select a tree-structure out of the graph
Breadth-first-search tree
Minimum spanning tree
Other domain-specific structures
Use a tree layout algorithm
Benefits
Fast, supports interaction and refinement
Drawbacks
Limited range of layouts

45. Tree-ify the graph

46. Traditional Tree Layouts
H-tree layout: best for balanced trees
Radial view
Balloon view: related to 3-d cone tree

47. Hierarchical graph layout
Use directed structure of graph to inform layout
Order the graph into distinct levels
this determines one dimension
Now optimize within levels
determines the second dimension
minimize edge crossings, etc
The method used in graphviz’s “dot” algorithm
Great for directed acyclic graphs, but often misleading in the case of cycles

48. Hierarchical Graph Layout
Evolution of the UNIX operating system
Hierarchical layering based on descent

49. Hierarchical graph layout
Gnutella network

50. Radial Layout
Animated Exploration of Graphs with Radial Layout, Yee et al., 2001
Gnutella network

51. Optimization-based layout
Specify constraints for layout
Series of mathematical equations
Hand to “solver” which tries to optimize the constraints
Examples
Minimize edge crossings, line bends, etc
Multi-dimensional scaling (preserve multi-dim distance)
Force-directed placement (use physics metaphor)
Benefits
General applicability
Often customizable by adding new constraints
Drawbacks
Approximate constraint satisfaction
Running time; “organic” look not always desired

52. Example: Force-Directed Layout
Uses physics model to layout graph,
Nodes repel each other, edges act as springs, and some amount of friction or drag force is used.
Special techniques to dampen “jitter”.
visual wordnet
visuwords

53. Hyperbolic Browser: Inspiration

54. Using Distortion and Focus + Context
The Hyperbolic Tree Browser
The Hyperbolic Browser: A Focus + Context Technique for Visualizing Large Hierarchies, Lamping & Rao, CHI 1996.
Uses non-Euclidean geometry as basis of focus + context technique
The hyperbolic browser is a projection into a Euclidean space – a circle
The circumference of a circle increases at a linearly with radius (2 PI)
The circumference of a circle in hyperbolic space increases exponentially
Exponential growth in space available with linear growth of radius
Makes tree layout easy
Size of objects decreases with growth of radius
Reduces expense of drawing trees when cut-off at one pixel

55. Appearance of Initial Layout
Root mapped at center
Multiple generations of children mapped out towards edge of circle
Drawing of nodes cuts off when less than one pixel
364 nodes – Uniform Distribution
1004 nodes – Poisson Distribution (i.e., more realistic)

56. Structurally-Independent Layout
Ignore the graph structure.
Base the layout on other attributes of the data
Examples:
Geography
Time
Benefits
Often very quick layout
Optimizes communication of particular features
Drawbacks
May or may not present structure well

57. Structurally Independent Layout
The “Skitter” Layout
Internet Connectivity
Angle = Longitude
geography
Radius = Degree
# of connections
Skitter,

58. References
David Gotz and Michelle X. Zhou: Characterizing users' visual analytic activity for insight provenance.
Information Visualization 8(1): 42-55, 2009.
David Gotz and Zhen Wen: Behavior-driven visualization recommendation. IUI 2009: , 2008.
Eser Kandogan: Just-in-time annotation of clusters, outliers, and trends in point-based data visualizations. IEEE VAST 2012:
Mengdie Hu, Huahai Yang, Michelle X. Zhou, Liang Gou, Yunyao Li, and Eben Haber: OpinionBlocks: A Crowd-Powered, Self-Improving Interactive Visual Analytic System for Understanding Opinion Text. To appear in Proc. INTERACT 2013.
Zhen Wen and Michelle X. Zhou: Evaluating the Use of Data Transformation for Information Visualization. IEEE Trans. Vis. Comp. Graph. 14(6): , 2008.
Zhen Wen and Michelle X. Zhou: An optimization-based approach to dynamic data transformation for smart visualization. IUI 2008: 70-79
Zhen Wen, Michelle X. Zhou, and Vikram Aggarwal: An Optimization-based Approach to Dynamic Visual Context Management. INFOVIS 2005:
Huahai Yang, Yunyao Li, and Michelle X. Zhou: A Crowd-sourced Study: Understanding Users’ Comprehension and Preferences for Composing Information Graphics. In Submission to TOCHI 2013.
Michelle X. Zhou and Min Chen: Automated Generation of Graphic Sketches by Example. IJCAI 2003: 65-74
Michelle X. Zhou, Min Chen, and Ying Feng: Building a Visual Database for Example-based Graphics Generation. INFOVIS 2002:
Michelle X. Zhou, Sheng Ma, and Ying Feng: Applying machine learning to automated information graphics generation. IBM Systems Journal 41(3): (2002)