Transcript Slide - Seidenberg School of Computer Science and Information

CSIS
School of Computer Science
and Information Systems
Group One
Data Visualization
Spring 2005
Doctor of Professional Studies in Computing
Agenda
I.
Overview
II.
Foundations of Visualization
III.
Visualization and KDD
IV.
I Can See Clearly Now
V.
XmdvTool Demonstration with ISBSG Case Study
I.
Overview
Visualize
"to form a mental vision, image, or picture of (something not visible or present to
sight, or of an abstraction); to make visible to the mind or imagination"
[The Oxford English Dictionary, 1989]
Many Variations "Visualization":
1) Visualization in Scientific Computing (Scientific Visualization)
2) Information Visualization
3) Software Visualization
Running Man
Fish Eating Boat
I.
Foundations of Visualization
III.
Visualization and KDD
• Knowledge Discovery from Databases
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Data Processing
Machine Learning
Evaluation
Visualization
• Experiments may be nested
• Approach Advocated by YALE
– Yet Another Learning Environment
– http://www-ai.cs.uni-dortmund.de/SOFTWARE/YALE
IV.
I Can See Clearly Now
Key Points:
•Data generation is exploding, particularly dimensional data
•Visualization takes place in context; tools and functionality are
driven by user needs and objectives
•Yang, et al provide an excellent baseline list of core and advanced
techniques for consideration
•Keim introduces an interesting 3-dimention view linking data type,
interaction technique, and display type
Data Growth Factoids:
•How much new information per person? According to the
Population Reference Bureau, the world population is 6.3 billion,
thus almost 800 MB of recorded information is produced per person
each year. It would take about 30 feet of books to store the
equivalent of 800 MB of information on paper.
•Information explosion? We estimate that new stored information
grew about 30% a year between 1999 and 2002
•The World Wide Web contains about 170 terabytes of information
on its surface; in volume this is seventeen times the size of the
Library of Congress print collections.
•Instant messaging generates five billion messages a day (750GB),
or 274 Terabytes a year.
•Email generates about 400,000 terabytes of new information each
year worldwide.
Visualization takes place in context – different users with
different needs have different requirement and techniques.
Use/need
“Richness” of Information
Managerial
Snap-shot
Interactive
reporting
“What If"
analysis
What Next ?
What should
I do ?
Visualization takes place in context – different users with
different needs have different requirement and techniques.
Typical Output
Use/need
“Richness” of Information
Managerial
Snap-shot
Managed
Metrics:
Scorecard &
Dashboards
Interactive
reporting
Enterprise
Reporting:
Navigation
needs and
reliable
information
“What If"
analysis
Mutlidimensional
“speed of
thought”
What Next ?
What should
I do ?
Analysis and
predictive
values
Prescribed
action:
Alerts and
notifications
Visualization takes place in context – different users with
different needs have different requirement and techniques.
Interaction
Typical Output
Use/need
“Richness” of Information
Managerial
Snap-shot
Interactive
reporting
Managed
Metrics:
Scorecard &
Dashboards
Enterprise
Reporting:
Navigation
needs and
reliable
information
Fixed display
Filter and
Zoom
What Next ?
What should
I do ?
Mutlidimensional
“speed of
thought”
Analysis and
predictive
values
Prescribed
action:
Alerts and
notifications
Slice & Dice,
Pivot tables
Derived
Information
Recommend
and Act
“What If"
analysis
Visualization takes place in context – different users with
different needs have different requirement and techniques.
Interaction
Typical Output
Use/need
“Richness” of Information
Managerial
Snap-shot
Interactive
reporting
Managed
Metrics:
Scorecard &
Dashboards
Enterprise
Reporting:
Navigation
needs and
reliable
information
Fixed display
Filter and
Zoom
Use the data to prove/disprove a hypothesis
What Next ?
What should
I do ?
Mutlidimensional
“speed of
thought”
Analysis and
predictive
values
Prescribed
action:
Alerts and
notifications
Slice & Dice,
Pivot tables
Derived
Information
Recommend
and Act
“What If"
analysis
Use the data to generate hypotheses
Yang, et al identify Core Navigation Tool:
•Filter – reduce the amount of data to increase focus
•Distortion – enlarge some part of a display to examine details
•Zooming and Panning – enlarge, make smaller, move through display
•Manual Pixel re-ordering – top to bottom, bottom to top
•Comparing – create/examine relationships
•Refining – generate a new, focused display of data subset
Yang, et al identify Advance Navigation Tool:
•Showing names – mouse-overs
•Layer re-ordering – ordering of overlapping data
•Manual relocation – separation of overlapping data
•Extent Scaling – interactive, proportional resizing
•Dynamic Masking – hiding of irrelevant data
•Automatic Shifting – automatic overlap reduction
Keim creates a 3-dimentional chart that relates interaction
technique, type of data, and visualization technique
Breakdown and examination of Keim model
Simple data
Complex data
Breakdown and examination of Keim model
Interaction
and
manipulation
techniques,
similar to
Yang
Breakdown and examination of Keim model
Recommended
display type
(some of
which we will
see in the
demos)
V.
XmdvTool Demonstration with ISBSG Case Study
• Tool Available at http://davis.wpi.edu/~xmdv
• Methods
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Scatterplots
Glyphs
Parallel Cordinates
Dimensional Stacking
• N-D Brush
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Highlight
Mask
Values
Average
Source of Case Study
• The International Software Benchmarking
Standards Group
– Mission – Help Improve Management of IT Resources
Through a Public Repository
– Produces – ISBSG Estimating, Benchmarking &
Research Suite (Release 8 in 2003) of Data and Tools
– Academic Use – Free or Nominal Charge
– Web Site – www.isbsg.org
• Same Source As Team One’s Data Mining Project
Composition of Study File
• 451 New Development Projects
• Fields
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Size in Adjusted Function Points
Duration in Months
Maximum Team Size
Work Effort in Hours
Project Delivery Rate