presentation

Visual Analytics as a
Translational Science
Brian Fisher
SFU School of Interactive Arts & Technology and
Program in Cognitive Science
UBC Media & Graphics Interdisciplinary Centre (MAGIC)
California Institute for Telecommunications & Information
Technology (Calit2)
Analytics & Analysis
• Where will computation, logic & math fail?
• Relevance, validity, reliability of data uncertain
• Assumptions of the model may not hold in given situation
• Multiple models to chose from
• Previously unknown pattern in data (data discovery)
• “Visually enabled reasoning”* can address this
• Used with mathematical or computational analytics
• Bridge HII to alternative (modal, hybrid) logics
• Emphasize discovery, mixed-initiative human/IS analysis
* Meyer J., Thomas, J., Diehl, S., Fisher, B., Keim, D., Laidlaw, D. Miksch S.,
Mueller, K. Ribarsky, W., Preim, B., & Ynnerman, A. (2010) From Visualization
to Visually Enabled Reasoning. In “Scientific Visualization: Advanced
Concepts”. vol. 1 pp. 227-245. Schloss Dagstuhl - Leibniz-Zentrum fuer
Informatik, Germany. I978-3-939897-19-4
Visual Analytics
“The science of analytical reasoning facilitated
by interactive visual interfaces”
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
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
Jim Thomas slide
Visual Analytics Trajectory
(my view)
• Increased multidisciplinarity (not just VAST!)
• Events at Cogsci, HICSS, eSocial Science
• 3/6 Canadian NSERC Strategic Partnership themes (5yr)
• Bridge to data sciences, machine learning
• NSF FODAVA
• EU 7th Framework VisMaster Coordination Action
• DFG Scalable VA Strategic
• More lab-to-”clinic” translational research
• Adapting to science-integrated tech design & evaluation
• Science to build analytics-- the process
• Lab methods are adapting to address research
questions from field work
Cognitive Science
Cognition
Perceptual
Sciences
Graphic &
Interaction
Design
Social
Sciences
“cognition
in the
wild”
Statistics &
Computation
My 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 (CIHR)
• Complex data in health research (CFRI)
• Public health monitoring & management (BC Injury
Research and Prevention Unit)
Understand cognition supported by
interactive visualization
• Visual expertise - how to assess,
Cognitive,
Perceptual
Sciences
model, teach, & build for it
• Individual differences, quantitative
predictions
• Visuomotor expertise - assess,
model, teach, & build for it
• Multimodality & modularity
(cognitive architecture)
Air traffic control research
• Free Flight ATC
“fishtank” projection
• Change camera
position for better
view
• How will global
motion affect
tracking?
Liu, G. Austen, E. L., Booth, K.S. Fisher, B., Argue, R. Rempel, M.I.,
& Enns, J. (2005) Multiple Object Tracking Is Based On Scene, Not
Retinal, Coordinates. Journal of Experimental Psychology: Human
Perception and Performance. 31(2), Apr 2005, 235-247.
Conclusion: Humans track in
allocentric space
• Retinal speed of targets does not
determine performance
• Motion of targets relative to each other
does
• But only if motion preserves good metric
characteristics of space
• Explanation is at the level of a human display cognitive system
Subject data for pointing
Po, B. Fisher, B. Booth, K. (2005) A Two Visual Systems Approach to
Understanding Voice and Gestural Interaction. Virtual Reality (Special
Issue on Language, Speech, and Gesture) 8, pg. 231-241.
Understand technological
distribution of cognition
• Build on social science (GT, JAT)
Social
Sciences
approaches to understand
organizations and cognitive work
practices.
• The innovation here is in the
extension of social science to
bridge to the perceptual and
cognitive science theories that
apply to the use of visualization in
analytical tasks.
Distributed Cognition
• “Pair analytics” sessions
• Student visual analyst & trained domain expert
•
collaborate on analytic task
• Student “drives”, expert “navigates”
Analysis of session based on knowledge of human
cognition & communication
• Analysis bridges socsci & cognitive science (Joint
Activity Theory & Grounded Theory)
• Investigating VA tech to support this (Boeing)
Arias-Hernandez, R, Kaastra, L.T., and Fisher, B. (2011) Joint Action Theory and
Pair Analytics: In-vivo Studies of Cognition and Social Interaction in Collaborative
Visual Analytics. In L. Carlson, C. Hoelscher, and T. Shipley (Eds.), Proceedings
of the 33rd Annual Conference of the Cognitive Science Society (pp. 3244-3249).
Austin TX: Cognitive Science Society.
Some Results
• Best debrief VAST
2007
• Discovery Exhibition
Best paper 2010
(Andrew Wade) &
2011 (Samar AlHajj)
• Analytics are
extracted and
communicated to
tech developers as
methods &
prototypes
In Memoriam: Andrew Wade, MSc.
Visweek 2010 Discovery Exhibi4on
Student Entry Award
Improving Airplane Safety: Tableau and Bird Strikes
Andrew Wade (SFU), Roger Nicholson (Boeing)
Andrew’s 4 month pair analy4cs internship with Boeing Safety Engineer Roger Nicholson made changes to 5 aircra> and the pilot training manual.
Andrew Wade defended his MSc. thesis “ Visual analy4cs for avia4on safety: A collabora4ve approach to sensemaking” in August 2011.
On October 6, 2011 Andrew was killed when his sightseeing flight to Mount Everest crashed in foggy condi4ons. Andrew’s MSc was awarded posthumously in October. It was accepted by his father, Don Wade.
Robust Visual Analytics
• A warrant (ala Toulmin) for process of
policy-making facilitated by interactive
visualization
• Data, its processing and arguments that result
• A visual analytics (not just visual analysis)
• Desiderata (at least) for communication between
analysts and policy-makers
• General principles derive from lab studies and
from multiple real-world cases
D-Cog VA Projects
• VR Design
Environment
(GMR)
• Fishtank VR air
traffic control
(Hughes/
Raytheon)
• Car interfaces
(Nissan)
• Perception of depth
in VR displays
(Fechner)
• Ability to track
targets in moving
spaces (FINST)
• Perception of touch
on the outer leg
• Discourse in “pair
analytics” (JAT)
Probs with current
• Too much solo analyst, too little
communication and collaboration.
•
•
•
Multiple stakeholders, roles, and knowledge
Bridge to organizational & societal systems
Tactics as well as strategy
• Poor integration of modelling and
visualization
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)
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•
•
•
2010 General Chair, VAST Conference
2010- VAST Steering Committee
2010 NSERC Strategic Grant “Visual Analytics for Emergency
Management”
How are VA systems
• 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
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,
Cognitive Systems Approach
• Cognitive System composed of
human
and computational cognitive processes
• Bound together through high-bandwidth
interface of vision/visualization for humaninformation 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 customdesigned processes for the exploration and