Literature DB >> 24051804

A multi-level typology of abstract visualization tasks.

Matthew Brehmer1, Tamara Munzner.   

Abstract

The considerable previous work characterizing visualization usage has focused on low-level tasks or interactions and high-level tasks, leaving a gap between them that is not addressed. This gap leads to a lack of distinction between the ends and means of a task, limiting the potential for rigorous analysis. We contribute a multi-level typology of visualization tasks to address this gap, distinguishing why and how a visualization task is performed, as well as what the task inputs and outputs are. Our typology allows complex tasks to be expressed as sequences of interdependent simpler tasks, resulting in concise and flexible descriptions for tasks of varying complexity and scope. It provides abstract rather than domain-specific descriptions of tasks, so that useful comparisons can be made between visualization systems targeted at different application domains. This descriptive power supports a level of analysis required for the generation of new designs, by guiding the translation of domain-specific problems into abstract tasks, and for the qualitative evaluation of visualization usage. We demonstrate the benefits of our approach in a detailed case study, comparing task descriptions from our typology to those derived from related work. We also discuss the similarities and differences between our typology and over two dozen extant classification systems and theoretical frameworks from the literatures of visualization, human-computer interaction, information retrieval, communications, and cartography.

Entities:  

Mesh:

Year:  2013        PMID: 24051804     DOI: 10.1109/TVCG.2013.124

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  12 in total

1.  Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments.

Authors:  Katy Börner; Andreas Bueckle; Michael Ginda
Journal:  Proc Natl Acad Sci U S A       Date:  2019-02-05       Impact factor: 11.205

2.  A Survey of Colormaps in Visualization.

Authors:  Liang Zhou; Charles D Hansen
Journal:  IEEE Trans Vis Comput Graph       Date:  2015-10-26       Impact factor: 4.579

3.  A Framework for Considering Comprehensibility in Modeling.

Authors:  Michael Gleicher
Journal:  Big Data       Date:  2016-06-07       Impact factor: 2.128

4.  Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields.

Authors:  Garnett W Bryant; Wesley Griffin; Judith E Terrill
Journal:  IEEE Trans Vis Comput Graph       Date:  2016-03-09       Impact factor: 4.579

5.  Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization.

Authors:  G Elisabeta Marai
Journal:  IEEE Trans Vis Comput Graph       Date:  2017-08-29       Impact factor: 4.579

6.  Scientists' sense making when hypothesizing about disease mechanisms from expression data and their needs for visualization support.

Authors:  Barbara Mirel; Carsten Görg
Journal:  BMC Bioinformatics       Date:  2014-04-26       Impact factor: 3.169

7.  A taxonomy of visualization tasks for the analysis of biological pathway data.

Authors:  Paul Murray; Fintan McGee; Angus G Forbes
Journal:  BMC Bioinformatics       Date:  2017-02-15       Impact factor: 3.169

8.  A Meta-Model Integration for Supporting Knowledge Discovery in Specific Domains: A Case Study in Healthcare.

Authors:  Andrea Vázquez-Ingelmo; Alicia García-Holgado; Francisco José García-Peñalvo; Roberto Therón
Journal:  Sensors (Basel)       Date:  2020-07-22       Impact factor: 3.576

9.  Task-Data Taxonomy for Health Data Visualizations: Web-Based Survey With Experts and Older Adults.

Authors:  Sabine Theis; Peter Wilhelm Victor Rasche; Christina Bröhl; Matthias Wille; Alexander Mertens
Journal:  JMIR Med Inform       Date:  2018-07-09

10.  Tasks, Techniques, and Tools for Genomic Data Visualization.

Authors:  S Nusrat; T Harbig; N Gehlenborg
Journal:  Comput Graph Forum       Date:  2019-07-10       Impact factor: 2.078

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