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A multi-level typology of abstract visualization tasks.

Matthew Brehmer¹, Tamara Munzner

¹University of British Columbia.

IEEE Transactions on Visualization and Computer Graphics

|September 21, 2013

Summary

This study introduces a new multi-level typology for visualization tasks, bridging the gap between low-level and high-level activities. This framework enables more rigorous analysis and design of visualization systems across different domains.

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Area of Science:

Information Visualization
Human-Computer Interaction
Data Analysis

Background:

Existing research on visualization tasks often focuses on either low-level interactions or high-level goals, creating a gap in understanding.
This lack of a middle ground hinders the distinction between task objectives and methods, limiting analytical rigor.

Purpose of the Study:

To introduce a novel multi-level typology of visualization tasks to address the gap between low-level and high-level task characterizations.
To provide a framework that distinguishes the 'why,' 'how,' and 'what' (inputs/outputs) of visualization tasks.
To enable concise, flexible, and abstract descriptions of visualization tasks for cross-domain comparison and analysis.

Main Methods:

Development of a multi-level typology for classifying visualization tasks.
Expressing complex tasks as sequences of simpler, interdependent tasks.
Conducting a case study to compare task descriptions generated by the typology against existing methods.
Comparing the proposed typology with over two dozen existing classification systems and theoretical frameworks.

Main Results:

The proposed typology offers abstract, domain-independent descriptions of visualization tasks.
Complex tasks can be broken down into interdependent simpler tasks, allowing for flexible and concise descriptions.
The typology facilitates the translation of domain-specific problems into abstract tasks, aiding new design generation and qualitative evaluation.
Demonstrated benefits through a case study comparing the typology with related work.

Conclusions:

The multi-level typology effectively bridges the gap in visualization task characterization.
This framework supports rigorous analysis, design generation, and qualitative evaluation of visualization usage.
The abstract nature of the typology allows for meaningful comparisons across diverse application domains and existing theoretical frameworks.