Download Definable Maps for Database Visualization

Architectural Support for
Database Visualization
Dennis Groth
Indiana University Computer Science
2002
Talk Structure
 Problem Motivation
 Overview of Visualization Process
 Mapping
 Summary and Future Research
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Motivation
 What is visualization?
“the act or process of interpreting in visual terms or
of putting into visible form.”
[Webster’s]
“Transforming the symbolic into the geometric.”
[McCormick et al, 1987]
“The binding (or mapping) of data to representations that
can be perceived.”
[Foley, 1994]
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The Goal of Visualization
 Gain insight into data
 Understand the “whole”
 Identify relationships
 Dynamic interaction
 Different than presentation graphics:

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Used to communicate information to others
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Scientific Visualization
 Visual representation of scientific data.
Rainfall in Peru over 3 day period
[Goldberg et al, 1987]
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Information Visualization
 Visual representation of abstract data.
[Document Spiral , Cugini et al]
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What’s the Difference?
 Scientific data:


Often, already numeric
Natural mapping to coordinates
 Abstract data:
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May not be numeric (No order, No scale)
Mappings must be defined or constructed
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Visualization Process (KDD)
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Architecture
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Applying a Map
Input Data
t1
t2
t3
.
.
.
tn
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Map
Output
m1
m2
m3
.
.
.
mn
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Database Activities
Sex
Salary
Age
…
M
640001
57
…
…
…
…
…
Salary
SalaryRank
640001
3318
…
…
SalaryRank
Select SalaryRank,Age, Count(*)
From Employee, SalaryMap
Where Employee.Salary = SalaryMap.Salary
Group By SalaryRank,Age
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Dennis Groth
Age
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User Interaction
 Rotation, Translation, Scaling
 Drill-down queries
 Select data-points for use in other contexts
 Scatterplots, barcharts, parallel coordinates,
surface plots, line, …
 Combining plots

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Scaled independently or dependently
Overlay, Offset, Tile
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Summary and Future Work




Architecture supporting visualization
Mapping language (Declarative approach)
Implementation
Future Research

Visualization

Extensions driven by applications
 Document search
 Cluster visualization

Data Mining

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Association rule discovery
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