Transcript Information Visualization - University of Technology, Sydney

Data & Information Visualization
Lecture 1:
Data, Information, Knowledge and
Their Presentations
Data & Information Visualization
Subject site:
http://staff.it.uts.edu.au/~maolin/32146_DIV/
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.
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
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.
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 > $1, 500 Insurance
 Modeling the world (Generalise)
[18 − 25] years old  P (accident) = high
Data mining  Knowledge
discovery
Knowledge
Timelines
Timelines
Knowledge
0
100
200
300
400
500
600
700
800
Utterances
0
100
200
300
400
500
600
700
800
Utterances
Visualization
of the output
Visualization
of the output
output
output
Data Mining
Algorithms
input
Data Mining
Algorithms
Visualization
of the input
input
Data
Data
Timelines
Knowledge
0
100
200
300
400
500
600
700
800
Utterances
Visualization
of the output
output
Intermediate
Visualization
Data Mining
Algorithms
Visualization
of the input
input
Data
Source
data
Integrated
datasets
Mapping
attributes
to visualisation
Visualisation
system
….
Generation of
visual models
Visual
models
Model selection
and validation
Model A
….
Model B
Reselection
Remapping
….
Analytical
techniques
Regenerating
• Decision trees
• Association
analysis
• Rule induction
• Clustering
• Graph statistics
Local URL
.com
.dk
24:00
Domains
Time
Visualization
Scientific Visualization
None Graph Visualization
Information Visualization
Graph Visualization
Graph G = (V, E)
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)
Reference Model
Visualization: Mapping from data to visual form
DATA
Data
VISUAL FORM
Data Tables
Data
Transformations
Visual
Structures
Visual
Mappings
Human Interaction
Views
View
Transformations
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 -> attribute; Case -> tuple or record
Data Tables (2)
 Variable Types
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N = Nominal
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O = Ordinal
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Unordered set
Ordered set
Q = Quantitative

Numeric range
 Metadata

Structure
Data Transformations
 Values  Derived Values
 Structure  Derived Structure
 Values  Derived Structure
 Structure  Derived Values
 Examples?
Visual Structures
 Data Tables are mapped to Visual Structures
 Expressive, effective
 Perception…and the human eye…
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.
Visual Presentations of data
None-relational data & Relational 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.)
An example of using SeeNet to view email data
volumes generated by AT&T long distance
network traffic. Edges represent email
connections. Weigh and colors of edges represent
volumes of email data.
Visual Structures (2)
 Spatial substrates
 Marks
 Graphical properties
Spatial Substrate
 Space is the container unto which other parts
of Visual Structure are poured.
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Composition
Alignment
Folding
Recursion
Overloading
Marks
 Points
 Lines
 Areas
 Volumes
 Graphs and Trees – to show relations or links
among objects
Graph-DrivenGraph
Visualization
of Relational Data
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.
Retinal Properties
 Type of graphical property
 Position/Size
 Gray Scale
 Orientation
 Color
 Texture
 Shape
Other Graphical Properties
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Crispness
Resolution
Transparency
Arrangement
Color: value, hue, saturation
Table 1.22
Finally, temporal encoding for visual structures
Attributed Visualization
Visualization of collaborative workspace
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
Human Interaction and
Transformation
 Direct Manipulation
 Controlling Mappings
Application1:Visual Web browser
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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
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].
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
user’s 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.
Application2:
File Management
and Site Mapping
Mapping to a Unix root with approx.
3700 directories and files
An example of using Space-Optimized
Tree Visualization for a small web site
mapping (approximately 80 pages)
- viewing techniques needed
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
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).
Application5:
Online Business
Process Management
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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
Application6:
Program Understanding
and Software Mining
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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
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