Data

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Data Information Visualization

• Lecture 1
• Data, Information, Knowledge and Their
  Presentations

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Data Information Visualization

• Subject site
• http//staff.it.uts.edu.au/maolin/32146_DIV/

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Data, Information, Knowledge

• Data thing a fundamental, indivisible thing in
  databases and data sets.
• Can be represented naturally by populations and
  labels.
• Associations between things.
• If an association can be described by a succinct,
  computable rule it is called an explicit
  association.
• If an association can not be described by a
  succinct, computable rule it is called an
  implicit association.
• An information thing is an implicit association
  between the data things.
• A knowledge thing is an explicit association
  between the data things or information things.

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Data, Information, Knowledge

• Data raw, uninterpreted facts
• Tom, 20 years old, student, turner
• Information relates items of Data
• Tom is 20 years old
• Knowledge relates items of Information
• Tom is 20 years old ? Tom pays gt 1, 500
  Insurance
• Modeling the world (Generalise)
• 18 - 25 years old ? P (accident) high

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Data mining ? Knowledge discovery
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Knowledge
Visualization of the output
output
Data Mining Algorithms
IntermediateVisualization
Visualization of the input
input
Data
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Visualization
Information Visualization
Scientific Visualization
None Graph Visualization
Graph Visualization
Graph G (V, E)
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The Definition of IV
Information visualization the use of
interactive visual representations of abstract,
non-physically based data to amplify cognition
CMS99. CMS99 Stuart K. Card, Jock D.
Mackinlay, and Ben Shneiderman. Readings in
information visualization using vision to think.
Morgan Kaufmann Publishers, Inc., 1999.
Xerox Palo Alto Research Center (PARC)
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Reference Model
Visualization Mapping from data to visual form
DATA
VISUAL FORM
Data
Data Tables
Visual Structures
Views
View Transformations
Data Transformations
Visual Mappings
Human Interaction
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Data Tables

• Relational descriptions of data extended to
  include metadata

Casei Casej Casek
Variablex Valueix Valuejx Valuekx
Variabley Valueiy Valuejy Valueky

Analogy to database Variable -gt attribute Case
-gt tuple or record
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Data Tables (2)

• Variable Types
• N Nominal
• Unordered set
• O Ordinal
• Ordered set
• Q Quantitative
• Numeric range
• Metadata
• Structure

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Data Transformations

• Values ? Derived Values
• Structure ? Derived Structure
• Values ? Derived Structure
• Structure ? Derived Values
• Examples?

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Visual Structures

• Data Tables are mapped to Visual Structures
• Expressive, effective
• Perceptionand the human eye

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Why do we need visual structures?
Maps, diagrams, and PERT charts are examples of
using visual representations to see things. A
good picture is worth ten thousand words.
Today, computers help people to see and
understand abstract data through pictures.
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Visual Presentations of data
None-relational data Relational data
An example of using SeeNet to view email data
volumes generated by ATT long distance network
traffic. Edges represent email connections. Weigh
and colors of edges represent volumes of email
data.
The little image dots represent data records of
the number of sun spots, from 1850 to 1993,
zoomed in on a small area. (collected from GVU
Center, Georgia I. T.)
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Visual Structures (2)

• Spatial substrates
• Marks
• Graphical properties

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Spatial Substrate

• Space is the container unto which other parts of
  Visual Structure are poured.
• Composition
• Alignment
• Folding
• Recursion
• Overloading

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Marks

• Points
• Lines
• Areas
• Volumes
• Graphs and Trees to show relations or links
  among objects

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Graph-Driven Visualization of Relational Data
Graph Visualization
An example of graph visualization. This is the
visualization of a family tree (graph). Here each
image node represents a person and the edges
represent relationships among these people in a
large family.
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Retinal Properties

• Type of graphical property
• Position/Size
• Gray Scale
• Orientation
• Color
• Texture
• Shape

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Other Graphical Properties

• Crispness
• Resolution
• Transparency
• Arrangement
• Color value, hue, saturation
• Table 1.22
• Finally, temporal encoding for visual structures

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Attributed Visualization
Visualization of collaborative workspace
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View Transformations

• Interactively modify and augment Visual
  Structures
• Location Probes
• Viewpoint Controls
• Zoom, pan, clip
• Overview an detail
• Distortions
• To perceive larger Visual Structure via distortion

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Human Interaction and Transformation

• Direct Manipulation
• Controlling Mappings

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Application1Visual Web browser

• WebOFDAV - mapping the entire Web,
• Look at the whole of WWW as one graph a huge and
  partially unknown graph.
• Maintain and display a subset of this huge graph
  incrementally.
• Reduce mouse-click rate
• Maintain a 2D map history of navigation

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The lost in hyperspace problem

• Even in this small document, which could be read
  in one hour, users experienced the lost in
  hyperspace phenomenon as exemplified by the
  following user comment I soon realized that if
  I did not read something when I stumbled across
  it, then I would not be able to find it later.
  Of the respondents, 56 agreed fully or partly
  with the statement, When reading the report, I
  was often confused about where I was. Nielson,
  1990.

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Visual Web Browser addresses the problem of lost
in hyperspace with a sense of space.

• Graphic Web Browser addresses the fundamental
  problem of lost in hyperspace by displaying a
  sequence of logical visual frames with a graphic
  history tail to track the users current
  location and keep records of his previous
  locations in the huge information space.
• The logical neighborhood of the focus nodes
  indicates the current location of the user, and
  the tail of history indicates the path of the
  past locations during the navigation.

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Application2File Managementand Site Mapping
An example of using Space-Optimized Tree
Visualization for a small web site mapping
(approximately 80 pages) - viewing techniques
needed
Mapping to a Unix root with approx. 3700
directories and files
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Application3 Web Reverse Engineering

• HWIT (Human Web Interface Tool) is able to reuse
  existing structures of web site by visualizing
  and modifying the corresponding web graphs, and
  then re-generating a new site by save the
  modified web graphs.

The layout of an existing structure of a web site
Enhancing the existing Web site by adding a
sub-site
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Application4 B2C e-Commerce

• VOS (Visual Online Shop) can be used for online
  grocery shopping, shopping cart model. It is
  applicable to any e-commerce shopping application
  (dynamically navigate e-catalogs).

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Application5 Online Business Process
Management

• WbIVC (Web-based Interactive Visual Component) is
  applied to a research project management system
  (RPMS) in universities.
• A participant can review the details of a
  specific process element by clicking on the
  corresponding rectangle, and then selecting the
  open a process element in the popup menu.
• A participant can also create a new artifact (a
  Java methods) to a research project by opening a
  edit window.

The output interface of the WbIVC in RPMS
The input interface of the WbIVC in RPMS
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Application6 Program Understandingand Software
Mining

• JavaMiner is for non-linear visual browsing of
  huge java code for programming understanding.
• textual data mining
• Visualize a variety of relationships between
  terms in Java code, e.g. HAS, SUBCLASS, CALL and
  INTERFACE relationships.
• Text documents, the lexicon, the neighborhood
  function

The input interface of the WbIVC in RPMS
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Conclusion

• Reference model approximates the basic steps for
  visualizing information
• Steps are an ongoing process with many iterations
• Goal of information visualization develop
  effective mappings to increase ability to
  think/to improve cognition