Data & Information Visualization

Lecture 1:

Data, Information, Knowledge and Data, Information, Knowledge and Their PresentationsTheir Presentations

Data & Information Visualization

Subject site:

http://staff.it.uts.edu.au/~maolin/32146_DIV/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. 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 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

Data mining Knowledge discovery

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

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

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

.com

.dk

Domains

Local URL

Time

24:00

Visualization Visualization

Information VisualizationScientific Visualization

None Graph Visualization Graph Visualization

Graph G = (V, E)

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)

Reference Model

Visualization: Mapping from data to visual form

Data Data TablesVisual

StructuresViews

Data Transformations

Visual Mappings

View Transformations

DATA VISUAL FORM

Human Interaction

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 N = Nominal

Unordered set O = Ordinal

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?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.

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

Visual Structures (2)

Spatial substrates Marks Graphical properties

Spatial Substrate

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

Marks

Points Lines Areas Volumes Graphs and Trees – to show relations or links

among objects

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

Retinal Properties

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

Other Graphical Properties

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: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

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 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.

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

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

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).

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

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

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