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