Post on 11-Nov-2014
description
Complexity ~ Visual Analytics
Brian FisherSchool of Interactive Arts & Technology and
Program in Cognitive Science
Translational Research
• Emergency Management (NSERC, DHS)– Mobile analytics / sensor analytics
– “Virtual EOC” visual analytic environment
• Aircraft Safety, Reliability (Boeing/MITACS)– “Pair analytics” of complex quant and text data
• Economics and finance (MITACS, NSF)– Behavioural economics (portfolios)
• Healthcare Monitoring & Management (DHS)– Complex data in health research (CFRI)
– Public health monitoring & management (BC Injury Research and Prevention Unit)
Visual Analytics
Tools support understanding implications of data Synthesize information & derive insight from massive, dynamic,
ambiguous, & conflicting data
Detect the expected & discover the unexpected
Build timely, defensible, & understandable assessments Communicate assessments effectively for action.
“The beginning of knowledge is the discovery of something we do not understand.” ~Frank Herbert (1920 - 1986)
Jim Thomas slide
“The science of analytical reasoning facilitated by interactive visual interfaces”
Dealing with Scale & Complexity
• Inferential statistical hypothesis testing
• Math/computational modelling– Strong solutions
– Cognitive architectures, neural nets, nonlinear dyamics
– Complexity science, systems science
• Visualization– Exploratory analysis, discovery
• Hybrid approaches?
Visualization in Science
• NSF: Visualization in Scientific Computing: McCormick, DeFanti, and Brown, Computer Graphics, November 1987
• IEEE Visualization conference 1990
“The purpose of [scientific] computing is insight, not numbers.” Richard Hamming
“Visualization is a method of computing.” Authors of report
Data Visualization
• Accurate spatial mapping
• Challenges –DSP–Graphics
Information Visualization
• 1990 Conference on diagrammatic reasoning
• 1995 InfoVis Conference– “Information
Visualization: Wings for the Mind” Stuart Card
Information Visualization (Card)
• External representations can support– Increasing the memory and processing resources
available to users
– Reducing the search for information by using visual representations to enhance the detection of patterns
– Engaging perceptual inference operations
– Using perceptual attention mechanisms for monitoring
– Supporting manipulation of information
http://www.visual-literacy.org/periodic_table/periodic_table.html
Big Data Example (Amaral)
Metabolism
Experts are overwhelmed by sheer volume and complexity of data
Cartographic Representation (Amaral)
Guimera & Amaral, Nature 433, 895 (2005)
Create data signature
Synthesize into high-dimensional discovery space
Visual discourse for discovery
One Core Concept(Examples from IN-SPIRE)
Battelle PNNL
Visualizing Medical Literature
Battelle PNNL
Time & Time & SpaceSpace
Atoms
Molecules
Gene Networks
Signaling Networks
Cells
Organism
Multi-Scale Analysis
FlowCytometry
Gene Expression MicroArrays
ProteinMicroArrays
LocalizationAssay (ChIP)
Bacterial Display for Rapid Peptide Ligand Isolation
luxSignaling
VA Research Approach
• National Visualization & Analytics Center (NVAC)– Battelle/PNNL 2004 R&D Agenda panel
• University: Brown, GMU, Georgia Tech, OSU, Penn State, Purdue,
SFU , Stanford, UC, UI, UM, UNC, UU, WPI
• Industry: Boeing, Microsoft, PARC, Sandia Labs
• Gov: CIA, DHS, FBI, NIST, NSA, unspecified
– Industry Consortium
– Regional Visualization Centres
– Centre of Excellence
• Ccicada (Rutgers DIMACS)
• VACCINE (Purdue et al)
Overview of the R&D Agenda
• Challenges
• Science of Analytical Reasoning
• Science of Visual Representations & Interactions
• Data Representations & Transformations
• Production, Presentation, & Dissemination
• Moving Research Into Practice
• Positioning for an Enduring Success
Visual analytics
• IEEE VAST
• DHS VACCINE/Ccicada
• DFG Scalable VA
• Eu 7th Framework VisMaster: Mastering the Information Age
• US National VA Centre (Battelle PNNL)
• Extended VA Community-- application-focused symposia & conferences
Visual Analytics Disciplines
• Statistics, data representation and statistical graphics
• Geospatial and Temporal Sciences
• Applied Mathematics
• Knowledge representation, management and discovery– Ontology, semantics, Natural Language Processing, extraction,
synthesis, …
• Cognitive and Perceptual Sciences
• Communication: Capture, Illustrate and present a message
• Decision sciences
• Information and Scientific VisualizationJim Thomas slide
How are VA systems different?
• Development based on understanding of expert cognition in situ– Informed by current cognitive & social science
– Engagement with community of experts
– Emergent cognitive science of expert reasoning
• Obvious support for analytical processes-- collaboration and interaction as well as observation– Graphical analog for analytic processes
– Support “Human-information discourse”
– Integrated across roles in the community
≠ ≠
Diversity of users ranging from novice ⇒ expert collaborating on tasks that require analysis, judgment, and coordination
Bill Buxton/Dave Kasik
...interactive visual interfaces
Extending analytics systems
• Coordinated technological, methodological, organizational & training support
• Many technologies w/o rich visualization-- small form factor devices, sensors, data input.
• Example: VA for Emergency Management NSERC SPP (+ 2 SPP companion proposals)– Population: cell phones
– First responders: blackberries
– Data fusion centres: geotagged sensor networks, big data processing
– Command centres: inteactive tabletops, walls, etc.
Bridging Computation & Application
• US DHS centre of excellence:– Ccicada: CCI Centre for Advanced Data Analysis
(DIMACS)
– VACCINE: VA for CCI (Purdue et al)
• Synergies of modelling & interactive vis?– Visual communication of model parameters & results
– Selecting among solutions
– Selecting modelling approach
– Manipulating parameters
– Fully mixed-initiative systems
• Role and training of analysts: VA cert, undergrad stream?
Visual Analytics Cognitive Systems
• Cognitive System composed of human and computational cognitive processes
• Bound together through high-bandwidth interface of vision/visualization for human-information discourse– Stream processor, many modular subprocessors working
in parallel, a learning system with large variation among individuals in methods, capabilities, and time course of processing
– Scalable visual analysis systems, automatic data analysis and interactive visualization for custom-designed processes for the exploration and analysis of complex information spaces.
Cognitive Science
CognitionPerceptualSciences
SocialSciences
Statistics & Computation
Graphic & Interaction
Design
“cognition in the wild”
My Involvement in VA• 2004 Contributor to the US National Research Agenda “Illuminating
the Path”
• 2006, 2007 Area Chair, Perception and Cognition IEEE Workshop on Visual Analytics Science and Technology (VAST)
• 2007-2009 NSERC Strategic Grant “Visual Analytics for Safety and Security”
• 2008- Steering Committee for German Priority Program “Scalable Visual Analytics, Invited talks at EuroVA, Dagstuhl Scientific Visualization, Scalable VA,
• 2009 Leadership Board, VACCINE (US Centre of Excellence in VA)
• 2010 General Chair, VAST Conference
• 2010- VAST Steering Committee
• 2010 NSERC Strategic Grant “Visual Analytics for Emergency Management”
Classical Cogsci
• Design guidelines & evaluation methods – Perceptual cognition
– Reasoning & problem solving
– Social cognition
– Cognitive linguistics
– Cognitive neuroscience
• Cognitive architecture models e.g. SOAR, ACT-R