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Visual Analytics. Lewis F. Jones III. Top 10 Observations for VA Technologies and Systems. Whole-Part Relationship Overall view of data Relationship Discovery Interaction and Explorative Techniques Combined Exploratory and Confirmatory Interaction Multiple Datatypes

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visual analytics

Visual Analytics

Lewis F. Jones III

top 10 observations for va technologies and systems
Top 10 Observations for VATechnologies and Systems
  • Whole-Part Relationship
    • Overall view of data
  • Relationship Discovery
    • Interaction and Explorative Techniques
  • Combined Exploratory and Confirmatory Interaction
  • Multiple Datatypes
  • Temporal Views and Interactions
    • Flowcharts, timelines, etc.
  • Groupings and Outlier Identification
  • Multiple Linked Views
    • Multiple views on one display
  • Labeling
  • Reporting
    • Explain analysis of data
  • Interdisciplinary Science
initial conditions for va challenges
Initial Conditions for VA Challenges
  • Untethered to Device/Network/Interaction
    • No dependence
  • Tethered to Data/Information
    • Use of multiple types and sources
  • Indefinite or Indeterminate Data
    • Tools will judge usefulness
  • Minimized Transaction Costs
    • Needs to be fast
  • Trust
    • Security
top 10 challenges for va
Top 10 Challenges for VA
  • Human-Information Discourse
    • “Walk-up usable” interfaces
    • Multi-device / Cross-platform
  • Collaborative Analytics
    • Not only evidential and confirmatory analytics, but also exploratory, hypothesis-driven, and predictive and proactive thinking
  • Holistic Visual Representations
    • Complete story at a glance
    • Effective labeling
    • Multi-type, multi-source data
  • Scale Independence
    • Enable reasoning over large, diverse information spaces to facilitate analytics and uncertainty refinement
  • Information Representations
    • Information synthesis (model + sensor)
    • Mathematical and semantically rich, data-preserving representations
top 10 challenges for va1
Top 10 Challenges for VA
  • Information Sharing
    • Share information securely between VA components and people with privacy-aware technologies
  • Active Information Products
    • Modifiable, reusable analytic components
  • Lightweight Software Architectures
    • Support and standards to rapidly develop VA applications and tools
  • Utility Evaluation
    • Evaluations of the utility of VA science, technology and systems
  • Sustaining Talent Base
    • Research, design and development continues
va stereotypes
VA Stereotypes
  • VA is adopted to primarily see and understand…
    • Massive Data
    • Complex Data
    • New Visual Paradigms
    • Hidden Insights
va realities
VA Realities
  • Massive Data
    • VA is equally useful with small and large data
    • People spend substantially more time working with small data sets than massive ones
    • VA should focus on data dimensionality rather than the number of observations
  • Complex Data
    • Most important questions are simple
    • Simple questions are answered much more quickly using VA
    • Even complex questions are often best answered using simple visualizations
  • New Visual Paradigms
    • Answering sophisticated questions does not require complex visual displays
    • A sequence of simple displays works just fine
  • Hidden Insights
    • VA should put more focus on saving users time rather than finding some hidden information
goals of analyzing data
Goals of Analyzing Data

Exploring

Cleaning

Gaining Confidence

Summarizing

Pursuing Inconclusive Paths

Confirming Facts

Presenting Findings

concepts to avoid
Concepts to Avoid

Difficult user interfaces

Lack of visual intelligence

Analytical inflexibility

Complicated architectures

insight
Insight
  • Spontaneous Insight – a moment of enlightenment in cognitive science; “eureka!”
    • Occurs subconsciously and isn’t a process that can be directly controlled, manipulated, or repeated
    • An event that can be experienced or had
  • Knowledge-Building Insight – an advance in knowledge or a piece of information
    • A substance that can be discovered, gained, or provided
insight1
Insight
  • Visualization should promote both types
  • Fours processes that lead to knowledge-building insight:
    • Provide overview
    • Adjust
    • Detect patterns
    • Match mental model
  • Deep, complex knowledge increases the possibility of spontaneous insight
  • Spontaneous insights increase the possibility of new directions for knowledge-building
  • Human learning is allowed to be flexible and scalable
insight management
Insight Management
  • VA approaches can be challenged by large amounts of insights
  • Insight Management becomes essential
    • Insight Recording
    • Insight Association
    • Insight Retrieval
    • Insight Exchange
  • Insight Characteristics:
    • Complex, deep, qualitative, unexpected, and relevant
  • Three Basic Components of Insights:
    • Set of information items
    • Specification describing how the information was gathered
    • Descriptive annotations
common insight management problems
Common Insight Management Problems
  • Requiring manual annotation
    • Time-consuming and tedious
    • Can be incomplete, imprecise, and hard to understand
  • Requiring manual relationship detection
    • Does not scale to a sense-making process with large amounts of insights, long analysis times, and multiple analysts
  • Hard to search for and reuse recorded insights
    • Different users may user different descriptive terms for an unregulated annotation process
  • Unsupported insight exchange in collaborative VA
    • Rely heavily on users to manually search and understand collaborators’ insights
insight description model
Insight Description Model
  • Three Components:
    • A fact extracted from analyzed data
      • Examples include outliers, patterns, and relationships
    • A mental model for evaluating the fact
    • Objective and subjective evaluations of the fact
  • Mental models are hard to do due to variations amongst data sets, applications, and analysts
  • Types of facts are predictable and independent from data sets, applications, and analysts
    • Examples include value/derived value, distribution, difference, extreme, rank, category, cluster, outlier, association, trend, compound fact, and meta fact
fact management framework
Fact Management Framework
  • Effectively and efficiently detect, annotate, associate, retrieve, and exchange facts using automatic or semi-automatic approaches
  • Fact taxonomy is created for categorizing facts
  • Fact taxonomies have the following criteria:
    • Completeness – cover the majority of the facts that can be discovered using the visualization tools under different conditions
    • Unambiguous – accurately and clearly distinguish fact types
    • Independence – separate from the applications and visualizations used to discover the facts
    • Utility – feasible for use in fact and insight management
fact management framework1
Fact Management Framework
  • Semi-Automatic Fact Annotation
    • Once a distinguished fact’s category is determined, the system knows what needs to be extracted from the data according to fact taxonomy attributes
  • Fact Organization, Indexing, Browsing, and Retrieval
    • Use keywords in the annotations, similar to YouTube
  • Fact Network
    • Constructed from annotation correlations and user modifications
  • Guided Fact Discovery
    • User notification upon fact discovery
  • Fact Exchange
    • User fills in a form to retrieve information, leaving attributes that are to be learned from blank
analytical discourse
Analytical Discourse
  • Process of constructing a collaborative plan by two or more agents
  • Three Structures:
    • Mechanical – segmental structure of analytical steps
    • Intentional – the way that discourse purposes relate
    • Focus of Attention
  • “SharedPlan” Theory of Collaboration
    • Knowledge of actions and the set of mental states held towards a plan
      • Intention, belief, and commitment
sharedplan theory of collaboration
“SharedPlan” Theory of Collaboration
  • A subplan becomes a Full SharedPlan when…
    • Participating agents have shared beliefs that all intend, and are committed to the whole plan
    • All actions on the leaf-nodes are basic actions
    • Parameters are already instantiated or another Full SharedPlan is ready for identifying the parameter
  • Partial SharedPlan is an ongoing discourse
  • Full SharedPlan is a completed discourse
  • Root plan is all encompassing, with subplans stemming from it identifying parameters and basic actions
sense making process
Sense-Making Process
  • Four primary stages:
    • Information Generation
      • Information and procedures for searching and analyzing the information are generated from a data source
      • Collaboration: Individual work for unhindered generation of comprehensive individual perspectives on the data
    • Schematization
      • Schemas created to guide iterations of categorization
      • Collaboration: Virtual sub-groups for comparison of findings
    • Argumentation and Shifting Schemas
      • Schemas are refined; outliers are discarded or form new schemas
      • Collaboration: Face-to-face meetings for physical analytical discourse
    • Decision Making
      • Schemas guide further analysis and generate questions and answers for the task being processed
      • Collaboration: Face-to-face meetings with simultaneous physical and virtual analytical discourse (using visualization tools together to answer questions)
collaborative va techniques
Collaborative VA Techniques
  • Collocated Collaboration
    • Large displays and shared workspaces
  • Synchronous Distance Work
    • Real-time networked displays
  • Asynchronous Collaboration
    • Partitioning work across time and space
    • Results in higher-quality outcomes such as broader discussions, more complete reports, and longer solutions than face-to-face collaborations due to the greater division of labor
asynchronous collaboration design considerations
Asynchronous Collaboration Design Considerations
  • Division and Allocation of Work
    • Modularity – how work is segmented into atomic units, parallelizing work into independent tasks (modules)
    • Granularity – module measurement of the cost or effort involved in performing a task
    • Cost of Integration – measurement of the cost or effort involved in synthesizing the contributions of each individual module
  • Common Ground and Awareness
    • Participants must be able to see the same visual environment in order to ground each others’ actions and comments
    • Bookmarking, or sharing specific states of the visualization, could be used
  • References and Deixis
    • General – a direction
    • Definite – named entities
    • Detailed – described by attributes
    • Deictic – pointing to a specific object, group, or region (indexing)
      • Pointing – some form of vectorial reference to direction attention
      • Placing – moving an object to a region that has shared, conventional meaning
asynchronous collaboration design considerations1
Asynchronous Collaboration Design Considerations
  • Incentives and Engagement
    • Monetary – material compensation such as a salary or reward
    • Hedonic – well-being or engagement experienced intrinsically
    • Social-psychological – perceived benefits such as increased status or social capital
  • Identity, Trust, and Reputation
    • A hypothesis suggested by a more trusted or reputable person will have a higher probability of being accepted as part of the group consensus
  • Group Dynamics
    • Explicit mechanisms for assisting group formation may aid collaboration
  • Consensus and Decision-Making
    • Agreement on the data to collect
    • How to organize and interpret data
    • Making decisions based upon the data
visualization tool design considerations
Visualization Tool Design Considerations
  • Some criticize tools for being too data-centric
    • They do not help users develop concepts and understanding from the results of visual exploration
  • Tools should be separated and coordinated into the following five areas…
    • Exploration
    • Analysis
    • Synthesis
    • Evaluation
    • Presentation
  • Ease of use
  • Keyword searchable data
  • File and information sharing
va project ideas
VA Project Ideas
  • Use RealXtend and/or Second Life to design and create a collaborative visual analytics tool set for data from any field of research
  • Use the ideas, considerations, and guidelines listed in this PowerPoint’s research
  • Focus on ease-of-use and speed rather than deep and extensive automatic analysis
  • Perform data calculations outside of the virtual reality environment, focusing only on displaying the information when within the VRE
    • Implement some data analysis techniques from COS 702
  • Use concepts learned during thesis work to better the design process and implementation
va project features
VA Project Features
  • Display VA data in 3D rather than flat graphs
    • Give the users options
      • Simple displays such as line graphs, bar graphs, pie charts, etc. (easier)
      • Complex displays such as radial tree maps, parallel coordinates, etc. (the real focus of the work)
      • Allow for quickly switching between display types on the fly (series of displays)
    • Utilize the second life environment features for visual understanding, such as colors and glow effects (do not go overboard!)
    • Make sure 3D is for better understanding; not for sake of 3D
    • This would avoid the complex creation of images using external tools
  • Tools for users to create extensive group work and history logging, visualization customization, and a tutorial / tooltips system
  • Be able to alert users to new insights and analysis as they sift through and manipulate data
  • Try to allow for plug-in support in some way
    • This is far off, but keep it in mind when designing
va project abstract
VA Project Abstract

Designing and implementing an asynchronous collaborative visual analytics toolset within a virtual reality environment, utilizing various three-dimensional, serial displays for efficient and effective understanding, interest, insight and analysis.

primary sources
Primary Sources

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Chabot, C. “Demystifying Visual Analytics.” IEEE Computer Graphics and Applications, Vol. 29, No. 2, March 2009, 84-87.

Chang, R., Ziemkiewicz, C., Green, T. M., and Ribarsky, W. “Defining Insight for Visual Analytics.” IEEE Computer Graphics and Applications, Vol. 29, No. 2, March 2009, 14-17.

Chen, Y., Yang, J., and Ribarsky, W. “Toward Effective Insight Management in Visual Analytics Systems.” In Proceedings of the 2009 IEEE Pacific Visualization Symposium (April 20 - 23, 2009). PACIFICVIS. IEEE Computer Society, Washington, DC, 49-56.

Ha, D., Kim, M., Wade, A., Chao, W. O., Ho, K., Kaastra, L., Fisher, B., and Dill, J. “From Tasks to Tools: A Field Study in Collaborative Visual Analytics.” In Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology (October 30 - November 01, 2007). Virtual reality, archeology, and cultural heritage. IEEE Computer Society, Washington, DC, 223-224.

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Thomas, J. and Kielman, J. “Challenges for Visual Analytics.” Foundations and Frontiers of Visual Analytics, Vol. 8, No. 4, Winter 2009, 309-314.

secondary sources
Secondary Sources

Aragon, C. R., Bailey, S. J., Poon, S., Runge, K. J., and Thomas, R. C. “Sunfall: A Collaborative Visual Analytics System for Astrophysics.” In Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology (October 30 - November 01, 2007). Virtual reality, archeology, and cultural heritage. IEEE Computer Society, Washington, DC, 219- 220.

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