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

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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.

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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

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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