Visual Analytics Education

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Visual Analytics Education

• Stuart Card
• PARC
• VAST
• Baltimore, MD
• 2 November 2006

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SIGCHI HCI Curriculum Group
Tom Hewett U Drexel Chair Ron Baecker U.
Toronto Stu Card PARC Tom Carey U. Guelph Jean
GasenVirginia Commonwealth U. Marilyn ManteiU.
Toronto Organizer Gary PerlmanOhio U. Gary
StrongDrexel U. Bill VerplankStanford U.
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Method
The Carnegie-Mellon Curriculum for Undergraduate
Computer Science Mary Shaw, Ed.
Define the field
Collect the results
Organize into an inventory
Break into courses
Organize courses into curricula
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Science B
Science A
Engineering Science C
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HCI Inventory
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U1. Social Organization and Work

• The human as an interacting social being. Nature
  of work. Mutual adaptation of human and
  technical systems as a whole.
• Points of view (eg., Rasmussens cognitive
  engineering)
• Models of human activity (eg, open procedures)
• Models of work, workflow, cooperative activity
• Socio-technical systems
• Quality of work life and job satisfaction

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C1. Input and Output Devices

• Technical construction of devices for mediating
  between humans and machines.
• Input devices survey, mechanics, performance
  characteristics, devices for disabled,
  handwriting gestures, speech, eye tracking,
  exotic devices
• Output devices survey, mechanics, vector-based,
  raster based, frame buffers, canvases, event
  handling, devices for disabled, sound speech,
  3D, motion, exotic devices
• Characteristics of input/output devices
• Virtual devices

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Building a Course
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A Curriculum Based in Computer Science
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Visual Analytics Inventory
USE AND CONTEXT
U1. Application Areas
HUMAN
COMPUTER
H1. Perception H2. Human Reasoning H3.
Attention H4. Collaboration
C1. Visualization C2. Analytic Methods C3. User
Modeling C4. Representation
D4. Example Systems and Case Studies
D2. ImplementationTechniques and Tools
D3. Evaluation
D1. Design Approaches
DEVELOPMENT PROCESS
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U1. Application Areas

• Knowledge Crystallization/Sensemaking
• Shopping
• Education
• Finance
• Security and Intelligence

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C1. Visualization
S. Card HCI Handbook
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C2. Analytic Methods

• Structure-Value transformation
• Decision theory
• Uncertainty
• Bayesian reasoning
• Machine learning

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C4. Representation

• Databases
• Datacubes and OLAP
• Textual analytics
• Semantic PipelinesLSI, spreading activation,
  ontologies, parsing, logical semantics
• Web analytics
• Graph methods
• Geographical Information Systems

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Visualization Reference Model
Visual Form
Data
Task
VisualStructures
Views
RawData
DataTables
View Transformations
Visual Mappings
Data Transformations
Human Interaction (controls)
Textbook instantiations --Card, Mackinlay,
Shneiderman (1999) Toolkit instantiations --prefu
se --Tableau
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Entity Workspace Evidence File
Workspace and HII

• Captures evidence rapidly using snap-together
  knowledge
• Recommends next steps using spreading activation
• Captures many entity relationship with low
  screen clutter
• Inference tools help connect the dots
• Organizes reading with document trails

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The 3D Semantic World
Each component has its own analytic modeling
computation (eg., lighting model)
Lighting
Materials
Camera View
World
Shapes
Model
View
Behavior
Controller
Program to world simulation, Not direct effects
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Visual Analytics Reference Model
Documents
People
User Model
Organiza-tions
View
World
Places
Time/ Events
Projects
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Analytic Model for Documents (The Semantic
Pipeline)
Discourse Context Analysis
Facts v beliefsAssertions v. denials
Formal semantics language processing
?
Limited inference
?
Ontologies and Normalization
Ontology Use
RelationsSentence Condensation
?
Lexical Functional Grammar
Deep Analysis of Sentence Structure
Machine Learning PLSA ClassificationSpreadi
ng Activation
Statistical Text Analysis
AuthorityTopicsText Summaries Relations
?
Shallow Parsing
?
Entities
Finite State Parser
Raw documents
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