1. David S. Ebert ebertd@purdue.edu David S. Ebert ebertd@purdue.edu 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

2. September 2011 SFU, JIBC UBC Ind U Navajo Tech UW Stanford GaTech FIU JSU UT UHD Austin U Stuttgart VaTech NC UNCC A&T Penn St. Swansea U Purdue

3. 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

4. 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 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: Issues: Multi-scale, multi-system, multisource, massive, data & simulations 1 mm 1 kHz 1km 5min

5. September 2011 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

6. 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

7. 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!

8. 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

9. 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

10. 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

11. September 2011 Visual Analytics At Real-World Scale Utilize advanced HPC techniques to enable interactive spatiotemporal analysis (spatiotemporal clustering, prediction) Cluster-based and cloud- based solutions GPGPU solutions Develop easily usable HPC visual analytic environments Example: Longhorn Exascale Visual Analytic Platform 2048 compute cores (Nehalem quad-core) 512 GPUs (128 NVIDIA Quadro Plex S4s, each containing 4 NVIDIA FX 5800s) 13.5 TB of distributed memory 210 TB global file system

12. 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

13. 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)

14. 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

15. September 2011 Result: Wise Visual Analytical Environments – Insight and Answers Adapt analytics to integrate and perform with user- specified 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

16. 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

17. 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

18. September 2011 For Further Information www.VisualAnalytics-CCI.org vaccine@purdue.edu ebertd@purdue.edu