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Taxonomies of Visualization Techniques
CMPT 455/826 - Week 12, Day 2
w12d2 Sept-Dec 2009 1
A Framework for Visual Data Mining of Structures
By Schulz, Nocke, and Schumann
w12d2 Sept-Dec 2009 2
Design Criteria for a Visualization Toolset
• Generality– For different applications
– For various users
– Modular
• Flexibility– Flexible control mechanisms
– Visual queries
– Supports derived data
• Usability– Data abstraction
– Acceptable reply times
– Intuitive interface
w12d2 Sept-Dec 2009 3
Tool components
• They describe a number of tools to:
– Pre-process data before visualization
– Allow the user to interact with the visualization
– Provide an algorithmic kernel to develop various visualizations
– Provide post-processing
w12d2 Sept-Dec 2009 4
w12d2 Sept-Dec 2009 5
Pre-processingPre-processing
User InteractionUser Interaction
Post-processing
and Interaction
Post-processing
and Interaction
Algorithmic KernelAlgorithmic Kernel
A Knowledge Task-Based Framework
for Design and Evaluation ofInformation Visualizations
By Amar and Stasko
w12d2 Sept-Dec 2009 6
Typical Problems
• Limited Affordances– The operations afforded by many visualization systems– are equivalent to very simple database queries.
• Predetermined Representations– The representations employed by common visualizations are not
particularly agile, – supporting the formation of simplistic, static cognitive models – from elementary queries on typically historical, cross-sectional
data.
• Decline Of Determinism In Decision-Making– We live in a world that is not only dominated by information,– but also by uncertainty.
w12d2 Sept-Dec 2009 7
Bridging The Analytic Gaps: Knowledge Tasks
• The Use Of Taxonomies– the development of taxonomies for organizing low level tasks
that a visualization should facilitate, and automatically creating presentations that match these tasks to appropriate techniques
• Rationale-Based Tasks– relate data sets to the realms in which decisions are being made
• Worldview-Based Tasks– indirectly support formulation of a strategy for browsing a
visualization by providing insights as to what data should be explored to clarify certain relationships or test certain hypotheses.
w12d2 Sept-Dec 2009 8
Knowledge tasks and scenarios
• Expose Uncertainty
• Concretize Relationships
• Formulate Cause And Effect
• Determine Domain Parameters
• Explain Multivariate Trends
• Confirm Hypotheses
w12d2 Sept-Dec 2009 9
A Taxonomy of Tasks for Guiding the Evaluation of
Multidimensional Visualizations
By Valiati, Pimenta, and Freitas
w12d2 Sept-Dec 2009 10
Operations for analyzing data
• Locate: the user knows a dataset entry and indicate it by pointing or describing it.
• Identify: similar to locate but the user describe the dataset entry without knowing it previously.
• Distinguish: different objects should be presented as distinct visual items.
• Categorize: objects may be different because they belong to different categories, which should be described by the user.
• Cluster: the system may find out categories and objects belonging to them are shown linked or grouped together.
• Distribution: the user specifies categories and objects belonging to them are distributed among them.
• Rank: the user is asked to indicate the order of the objects displayed.
• Compare: the user is asked to compare entities based on their attributes.
• Compare within and between relations: the user is asked to compare similar entities or different sets of objects.
• Associate: the user is asked to establish relations between objects displayed.
• Correlate: the user may observe shared attributes between objects
• Based on: Wehrend and Lewis
w12d2 Sept-Dec 2009 11
w12d2 Sept-Dec 2009 12
Rethinking Visualization: A High-Level Taxonomy
By Tory and Möller
w12d2 Sept-Dec 2009 13
Classification of visualization tasks
w12d2 Sept-Dec 2009 14
The classification is broken down according to
• how much the spatialization is constrained and
•whether the design model is continuous or discrete (with or without structure).
Colours match figure text to outlined / shaded areas