Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.
-
Upload
derek-campbell -
Category
Documents
-
view
230 -
download
2
Transcript of Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.
![Page 1: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/1.jpg)
Data & Information Visualization
Lecture 1:
Data, Information, Knowledge and Data, Information, Knowledge and Their PresentationsTheir Presentations
![Page 2: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/2.jpg)
Data & Information Visualization
Subject site:
http://staff.it.uts.edu.au/~maolin/32146_DIV/http://staff.it.uts.edu.au/~maolin/32146_DIV/
![Page 3: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/3.jpg)
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.
![Page 4: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/4.jpg)
Data, Information, Knowledge
Data: raw, uninterpreted factsTom, 20 years old, student, turner
Information relates items of DataTom is 20 years old
Knowledge relates items of InformationTom is 20 years old Tom pays > $1, 500 Insurance
Modeling the world (Generalise)[18 − 25] years old P (accident) = high
![Page 5: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/5.jpg)
Data mining Knowledge discovery
![Page 6: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/6.jpg)
Data
Data Mining Algorithms
Visualization of the output
Knowledge
output
input
0 100 200 300 400 500 600 700 800
Utterances
Tim
elin
es
Data
Data Mining Algorithms
Visualization of the output
Knowledge
output
input
0 100 200 300 400 500 600 700 800
Utterances
Tim
elin
es
Visualization of the input
![Page 7: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/7.jpg)
Data
Data Mining Algorithms
Visualization of the output
Knowledge
output
input
0 100 200 300 400 500 600 700 800
Utterances
Tim
elin
es
Visualization of the input
IntermediateVisualization
![Page 8: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/8.jpg)
Mapping attributes
to visualisation
Sourcedata
Reselection
Visualisationsystem
….
….
Visualmodels
Generation of visual models
Remapping
….
Model A
Model B
Analyticaltechniques
Model selection and validation
Regenerating
Integrateddatasets
• Decision trees• Association analysis• Rule induction• Clustering• Graph statistics
![Page 9: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/9.jpg)
![Page 10: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/10.jpg)
![Page 11: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/11.jpg)
![Page 12: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/12.jpg)
.com
.dk
Domains
Local URL
Time
24:00
![Page 13: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/13.jpg)
Visualization Visualization
Information VisualizationScientific Visualization
None Graph Visualization Graph Visualization
Graph G = (V, E)
![Page 14: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/14.jpg)
The Definition of IVThe Definition of IV
Information visualization: the use of interactive visual Information visualization: the use of interactive visual representations of abstract, non-physically based data representations of abstract, non-physically based data to amplify cognition [CMS99].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)
![Page 15: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/15.jpg)
Reference Model
Visualization: Mapping from data to visual form
Data Data TablesVisual
StructuresViews
Data Transformations
Visual Mappings
View Transformations
DATA VISUAL FORM
Human Interaction
![Page 16: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/16.jpg)
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
![Page 17: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/17.jpg)
Data Tables (2)
Variable Types N = Nominal
Unordered set O = Ordinal
Ordered set Q = Quantitative
Numeric range
Metadata Structure
![Page 18: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/18.jpg)
Data Transformations
Values Derived Values Structure Derived Structure Values Derived Structure Structure Derived Values
Examples?
![Page 19: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/19.jpg)
Visual Structures
Data Tables are mapped to Visual Structures Expressive, effective Perception…and the human eye…
![Page 20: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/20.jpg)
Why do we need visual structures?Why do we need visual structures?
Maps, diagrams, and PERT charts are examples of using Maps, diagrams, and PERT charts are examples of using visual representations to see things. visual representations to see things. A good picture is worth A good picture is worth ten thousand words. ten thousand words.
Today, computers help people to Today, computers help people to see and understand abstract see and understand abstract data through pictures.data through pictures.
![Page 21: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/21.jpg)
Visual Presentations of dataVisual Presentations of 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.
None-relational data & Relational data None-relational data & Relational data
![Page 22: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/22.jpg)
Visual Structures (2)
Spatial substrates Marks Graphical properties
![Page 23: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/23.jpg)
Spatial Substrate
Space is the container unto which other parts of Visual Structure are poured. Composition Alignment Folding Recursion Overloading
![Page 24: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/24.jpg)
Marks
Points Lines Areas Volumes Graphs and Trees – to show relations or links
among objects
![Page 25: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/25.jpg)
Graph-Driven Visualization of Relational DataGraph-Driven Visualization of Relational Data
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.
Graph VisualizationGraph Visualization
![Page 26: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/26.jpg)
Retinal Properties
Type of graphical property Position/Size Gray Scale Orientation Color Texture Shape
![Page 27: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/27.jpg)
Other Graphical Properties
Crispness Resolution Transparency Arrangement Color: value, hue, saturation Table 1.22 Finally, temporal encoding for visual structures
![Page 28: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/28.jpg)
Attributed Visualization
Visualization of collaborative workspace
![Page 29: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/29.jpg)
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
![Page 30: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/30.jpg)
Human Interaction and Transformation
Direct Manipulation Controlling Mappings
![Page 31: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/31.jpg)
Application1:Application1:Visual Web browserVisual 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
![Page 32: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/32.jpg)
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].
![Page 33: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/33.jpg)
Visual Web Browser addresses the problem of Visual Web Browser addresses the problem of “lost in hyperspace” with a sense of “space”.“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.
![Page 34: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/34.jpg)
Application2:Application2:File ManagementFile Managementand Site Mappingand 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
![Page 35: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/35.jpg)
Application3: Application3: Web Reverse EngineeringWeb 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
![Page 36: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/36.jpg)
Application4: Application4: B2C e-CommerceB2C 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).
![Page 37: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/37.jpg)
Application5: Application5: Online Business Online Business
Process ManagementProcess 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
![Page 38: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/38.jpg)
Application6: Application6: Program UnderstandingProgram Understanding
and Software Miningand 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
![Page 39: Data & Information Visualization Lecture 1: Data, Information, Knowledge and Their Presentations.](https://reader035.fdocuments.net/reader035/viewer/2022081519/56649ddb5503460f94ad1d98/html5/thumbnails/39.jpg)
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