Visual Analytics to Enable
Discovery and Decision Making:
Potential, Challenges, and
Directions
Some material courtesy of
Alan MacEachren, Bill Ribarsky, Antonio
Sanfilippo, Kelly Gaither, Min Chen, Tom Ertl,
Sonia Lasher-Trapp, Daniel Keim
David S. Ebert
ebertd@purdue.edu
SFU, JIBC
UBC
UW
Purdue
Penn St.
Ind U
VaTech
Stanford
NC UNCC
A&T
Navajo
Tech
GaTech
JSU
U
Stuttgart
UT UHD
Austin
Swansea U
FIU
September 2011
Motivation
To solve today’s and tomorrow’s problems
requires exploring, analyzing, and reasoning
with massive, multisource, multiscale,
heterogeneous, streaming data
Image of Analyst’s Notebook
September 2011
Atmospheric Science: Multi-scale
Interactions (in the words of a cloud physicist)
No observing platform can measure the quantities of
interest over all needed spatial and temporal scales
needed
1 mm
1 kHz
No numerical model can simulate the quantities of
interest over all needed spatial and temporal scales
We observe/simulate over a subset of the pertinent
scales, using different instruments/models, and must
assimilate these results to understand the “big picture”
Visual analytics is crucial for this task
Issues: Multi-scale, multi-system, multisource,
massive, data & simulations
September 2011
1km
5min
One Solution in Use: Our Atmospheric
Visual Analytic Environment
Utilize multiple rendering
styles
Provide interactive data
exploration and user
directed analysis
Allow user specified
analysis and queries on
the fly
Allow interactive
correlative analysis of
multisource data
September 2011
What Visual Analytics Enables
•Enable effective decision making through
interactive visual analytic environments
•Enable effective communication of information
•Provide quantitative, reliable, reproducible evidence
•Enable user to be more effective from planning to
detection to response to recovery
•Enable proactive and predictive visual
analytics
September 2011
What’s Needed for Proactive and
Predictive Visual Analytics?
•Reliable and reproducible models and simulation
•Understanding of the data
• Distribution and skewness, errors, appropriate analysis
techniques
•Understanding of the sources and types of data
•Comparable or Correlative sources data
• Appropriate transformations applies to enable meaningful
comparison and correlation
•Understanding of the use and problem to be
solved!
September 2011
Four Challenges for Proactive &
Predictive Visual Analytics at Scale
1. Computer-human visual cognition environments
2. Interactive simulation and analytics
3. Specific scale issues
4. Uncertainty and time
September 2011
Integrated Computer-Human Visual
Cognition Environments
Balance of automated computerized
analysis and human cognition to amplify
human-centered decision making
Leverage both
• Human knowledge and visual analysis to
increase analytical efficiency and guide simulations and analysis
• Interactive simulations, dimensional reduction, clustering,
analytics to improve decision making
Create interactive discovery, planning & decision making
environments
Discover knowledge about role of visual display
and interfaces in discovery and decision-making
September 2011
Integrated Interactive Simulations and
Analysis
Analysis and simulation must be interactive for
integration into interactive environment
Need novel computational & statistical models
Goal: enable improved discovery, decision
making, analysis, and evaluation
September 2011
Visual Analytics At Real-World Scale
•Utilize advanced HPC
techniques to enable
interactive spatiotemporal
analysis (spatiotemporal
clustering, prediction)
•Example: Longhorn
Exascale Visual Analytic
Platform
• 512 GPUs (128 NVIDIA Quadro
• Cluster-based and cloudbased solutions
Plex S4s, each containing 4
NVIDIA FX 5800s)
• GPGPU solutions
•Develop easily usable
HPC visual analytic
environments
• 2048 compute cores (Nehalem
quad-core)
• 13.5 TB of distributed memory
• 210 TB global file system
September 2011
Scale: Multiscale Visual Analytics
Data at multiple semantic and physical scales must be
integrated and analyzed to produce scalable solutions for
all scales of the problem
Utilize natural problem scales
Enable cross-scale visual analysis
Enable decision making and action at all scales needed (e.g.,
neighborhood-city-state-nation, genome-cell-organ-body)
Interactive multisource, multiscale,
multimedia analysis and integration
of massive and streaming data
September 2011
Uncertainty and Temporal VA
Challenges
Integrated, interactive temporal analytics
• Novel, interactive temporal analytical techniques
Intuitive reasoning and analysis across time
and space
Precise information managing uncertainty
Temporal visual representations that
provide context and do not
introduce a propensity effect
(e.g., from animation)
September 2011
Integrated Interactive Predictive
Temporal Visual Analytics
Creating what-if and consequence evaluation
environments with measures of certainty
Challenge:
• Develop natural interactive visual spatiotemporal environments
–Seamless and natural interaction with and representation of
temporal data
–Novel multivariate, multidimensional visual representations
and analysis
September 2011
Result: Wise Visual Analytical
Environments – Insight and Answers
Adapt analytics to integrate and perform with userspecified
• Context
• Constraints and boundaries
Incorporate analyst’s knowledge
Incorporate resources for planning, discovery,
action
"Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?"
T. S. Eliot
September 2011
Result: Wise Integrated Interactive
Predictive Visual Analytics
Challenges
• Scalable representation across problem scales
and user scales
• User-guided correlative and predictive analysis
• New temporal, spatiotemporal, precise,
multivariate, and streaming analytical techniques
September 2011
Keys for Success
•User and problem driven
•Balance human cognition and automated analysis
and modeling
• Often applied on-the-fly for specific components identified by the user
•Interactivity and easy interaction
• Utilizing HPC and novel analysis approaches
•Understandability of why predicted value is what it
is
•Intuitive visual cognition
•Not overloaded with features
September 2011
For Further Information
www.VisualAnalytics-CCI.org
vaccine@purdue.edu
ebertd@purdue.edu
September 2011