LEWIS F. JONES I I I

Visual Analytics

Top 10 Observations for VATechnologies and Systems

1. Whole-Part Relationship• Overall view of data

2. Relationship Discovery• Interaction and Explorative Techniques

3. Combined Exploratory and Confirmatory Interaction4. Multiple Datatypes5. Temporal Views and Interactions

• Flowcharts, timelines, etc.6. Groupings and Outlier Identification7. Multiple Linked Views

• Multiple views on one display8. Labeling9. Reporting

• Explain analysis of data10. Interdisciplinary Science

Initial Conditions for VA Challenges

a. Untethered to Device/Network/Interaction• No dependence

b. Tethered to Data/Information• Use of multiple types and sources

c. Indefinite or Indeterminate Data• Tools will judge usefulness

d. Minimized Transaction Costs• Needs to be fast

e. Trust• Security

Top 10 Challenges for VA

1. Human-Information Discourse• “Walk-up usable” interfaces• Multi-device / Cross-platform

2. Collaborative Analytics• Not only evidential and confirmatory analytics, but also exploratory,

hypothesis-driven, and predictive and proactive thinking3. Holistic Visual Representations

• Complete story at a glance• Effective labeling• Multi-type, multi-source data

4. Scale Independence• Enable reasoning over large, diverse information spaces to facilitate analytics

and uncertainty refinement5. Information Representations

• Information synthesis (model + sensor)• Mathematical and semantically rich, data-preserving representations

Top 10 Challenges for VA

6. Information Sharing• Share information securely between VA components and people with

privacy-aware technologies7. Active Information Products

• Modifiable, reusable analytic components8. Lightweight Software Architectures

• Support and standards to rapidly develop VA applications and tools9. Utility Evaluation

• Evaluations of the utility of VA science, technology and systems10. Sustaining Talent Base

• Research, design and development continues

VA Stereotypes

VA is adopted to primarily see and understand… Massive Data Complex Data New Visual Paradigms Hidden Insights

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

ExploringCleaningGaining ConfidenceSummarizingPursuing Inconclusive PathsConfirming FactsPresenting Findings

Concepts to Avoid

Difficult user interfacesLack of visual intelligenceAnalytical inflexibilityComplicated architectures

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

Insight

Visualization should promote both typesFours 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

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

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 analystsHard 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

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

Effectively and efficiently detect, annotate, associate, retrieve, and exchange facts using automatic or semi-automatic approaches

Fact taxonomy is created for categorizing factsFact 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 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

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

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 parameterPartial SharedPlan is an ongoing discourseFull SharedPlan is a completed discourseRoot plan is all encompassing, with subplans

stemming from it identifying parameters and basic actions

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

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

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 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 capitalIdentity, 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

Some criticize tools for being too data-centric They do not help users develop concepts and understanding from

the results of visual explorationTools should be separated and coordinated into the

following five areas… Exploration Analysis Synthesis Evaluation Presentation

Ease of useKeyword searchable dataFile and information sharing

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

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

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.

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