Literature DB >> 20224139

Hierarchical aggregation for information visualization: overview, techniques, and design guidelines.

Niklas Elmqvist1, Jean-Daniel Fekete.   

Abstract

We present a model for building, visualizing, and interacting with multiscale representations of information visualization techniques using hierarchical aggregation. The motivation for this work is to make visual representations more visually scalable and less cluttered. The model allows for augmenting existing techniques with multiscale functionality, as well as for designing new visualization and interaction techniques that conform to this new class of visual representations. We give some examples of how to use the model for standard information visualization techniques such as scatterplots, parallel coordinates, and node-link diagrams, and discuss existing techniques that are based on hierarchical aggregation. This yields a set of design guidelines for aggregated visualizations. We also present a basic vocabulary of interaction techniques suitable for navigating these multiscale visualizations.

Mesh:

Year:  2010        PMID: 20224139     DOI: 10.1109/TVCG.2009.84

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


  4 in total

1.  iHAT: interactive hierarchical aggregation table for genetic association data.

Authors:  Julian Heinrich; Corinna Vehlow; Florian Battke; Günter Jäger; Daniel Weiskopf; Kay Nieselt
Journal:  BMC Bioinformatics       Date:  2012-05-18       Impact factor: 3.169

2.  Cuttlefish: Color Mapping for Dynamic Multi-Scale Visualizations.

Authors:  N Waldin; M Waldner; M Le Muzic; E Gröller; D S Goodsell; L Autin; A J Olson; I Viola
Journal:  Comput Graph Forum       Date:  2019-03-26       Impact factor: 2.078

3.  Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions.

Authors:  Sugeerth Murugesan; Kristofer Bouchard; Edward Chang; Max Dougherty; Bernd Hamann; Gunther H Weber
Journal:  BMC Bioinformatics       Date:  2017-06-06       Impact factor: 3.169

4.  AVOCADO: Visualization of Workflow-Derived Data Provenance for Reproducible Biomedical Research.

Authors:  H Stitz; S Luger; M Streit; N Gehlenborg
Journal:  Comput Graph Forum       Date:  2016-07-04       Impact factor: 2.078

  4 in total

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