article

Scale and complexity in visual analytics

Authors:

George Robertson,

Ken JoyAuthors Info & Claims

Information Visualization, Volume 8, Issue 4

Pages 247 - 253

https://doi.org/10.1057/ivs.2009.23

Published: 01 December 2009 Publication History

Abstract

The fundamental problem that we face is that a variety of large-scale problems in security, public safety, energy, ecology, health care and basic science all require that we process and understand increasingly vast amounts and variety of data. There is a growing impedance mismatch between data size/complexity and the human ability to understand and interact with data. Visual analytic tools are intended to help reduce that impedance mismatch by using analytic tools to reduce the amount of data that must be viewed, and visualization tools to help understand the patterns and relationships in the reduced data. But visual analytic tools must address a variety of scalability issues if they are to succeed. In this paper, we characterize the scalability and complexity issues in visual analytics. We discuss some highlights on progress that has been made in the past 5 years, as well as key areas where more progress is needed.

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Information & Contributors

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Published In

cover image Information Visualization

Information Visualization Volume 8, Issue 4

December 2009

76 pages

ISSN:1473-8716

Issue’s Table of Contents

Publisher

Palgrave Macmillan

Publication History

Published: 01 December 2009

Accepted: 07 July 2009

Revised: 07 July 2009

Received: 27 May 2009

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Cited By

Richer GPister AAbdelaal MFekete JSedlmair MWeiskopf D(2024)Scalability in VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.323123030:7(3314-3330)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TVCG.2022.3231230
Deng DCui WMeng XXu MLiao YZhang HWu Y(2023)Revisiting the Design Patterns of Composite VisualizationsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.321356529:12(5406-5421)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TVCG.2022.3213565
Cakmak EJäckle DSchreck TKeim DFuchs J(2022)Multiscale Visualization: A Structured Literature AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.310938728:12(4918-4929)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1109/TVCG.2021.3109387
Murugesan SKiran MHamann BWeber G(2022)Netostat: analyzing dynamic flow patterns in high-speed networksCluster Computing10.1007/s10586-022-03543-025:4(2915-2930)Online publication date: 1-Aug-2022
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Zhang JAhlbrand BMalik AChae JMin ZKo SEbert D(2016)A Visual Analytics Framework for Microblog Data Analysis at Multiple Scales of AggregationComputer Graphics Forum10.5555/3071534.307158135:3(441-450)Online publication date: 1-Jun-2016
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Winters KLach DCushing J(2014)Considerations for characterizing domain problemsProceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization10.1145/2669557.2669573(16-22)Online publication date: 10-Nov-2014
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Bieh-Zimmert OFelden CAl Motairy OChbeir RAlodib MLaouamer LBadr YBenyoucef MHarous SBiskri I(2014)Shaping Unlimited PatternsProceedings of the 6th International Conference on Management of Emergent Digital EcoSystems10.1145/2668260.2668279(72-77)Online publication date: 15-Sep-2014
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Light RPolley DBörner K(2014)Open data and open code for big science of science studiesScientometrics10.1007/s11192-014-1238-2101:2(1535-1551)Online publication date: 1-Nov-2014
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Kasik DEbert DLebanon GPark HPottenger W(2009)Data transformations and representations for computation and visualizationInformation Visualization10.1057/ivs.2009.278:4(275-285)Online publication date: 1-Dec-2009
https://dl.acm.org/doi/10.1057/ivs.2009.27

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