Original Article

Information Visualization (2009) 8, 275–285. doi:10.1057/ivs.2009.27

Data transformations and representations for computation and visualization

This article is a product of a workshop on the Future of Visual Analytics, held in Washington, DC on 4 March 2009. Workshop attendees included representatives from the visual analytics research community across government, industry and academia. The goal of the workshop, and the resulting article, was to reflect on the first 5 years of the visual analytics enterprise and propose research challenges for the next 5 years. The article incorporates input from workshop attendees as well as from its authors.

David J Kasika, David Ebertb, Guy Lebanonc, Haesun Parkc and William M Pottengerd

  1. aThe Boeing Company, PO Box 3707, Seattle, WA 98124, USA
  2. bPurdue University, West Lafayette, IN 47907, USA
  3. cGeorgia, Institute of Technology, Atlanta, GA 30332, USA
  4. dRutgers University, Piscataway, NJ 08854, USA

Correspondence: David J Kasik, E-mail: david.j.kasik@boeing.com

Received 27 May 2009; Revised 8 July 2009; Accepted 8 July 2009.

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Abstract

At the core of successful visual analytics systems are computational techniques that transform data into concise, human comprehensible visual representations. The general process often requires multiple transformation steps before a final visual representation is generated. This article characterizes the complex raw data to be analyzed and then describes two different sets of transformations and representations. The first set transforms the raw data into more concise representations that improve the performance of sophisticated computational methods. The second transforms internal representations into visual representations that provide the most benefit to an interactive user. The end result is a computing system that enhances an end user's analytic process with effective visual representations and interactive techniques. While progress has been made on improving data transformations and representations, there is substantial room for improvement.

Keywords:

algorithms, visual metaphors, data characteristics, visual representations, synthesis

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Interactive Visualization and Data Analysis, Masters program at Danube University Krems, Austria