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Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis

C. Turkay, E. Kaya, S. Balcisoy, H. Hauser

ARTICLE, IEEE Transactions on Visualization and Computer Graphics, jan, 2017

Abstract

In interactive data analysis processes, the dialogue between the human and the computer is the enabling mechanism that can lead to actionable observations about the phenomena being investigated. It is of paramount importance that this dialogue is not interrupted by slow computational mechanisms that do not consider any known temporal human-computer interaction characteristics that prioritize the perceptual and cognitive capabilities of the users. In cases where the analysis involves an integrated computational method, for instance to reduce the dimensionality of the data or to perform clustering, such non-optimal processes are often likely. To remedy this, progressive computations, where results are iteratively improved, are getting increasing interest in visual analytics. In this paper, we present techniques and design considerations to incorporate progressive methods within interactive analysis processes that involve high-dimensional data. We define methodologies to facilitate processes that adhere to the perceptual characteristics of users and describe how online algorithms can be incorporated within these. A set of design recommendations and according methods to support analysts in accomplishing high-dimensional data analysis tasks are then presented. Our arguments and decisions here are informed by observations gathered over a series of analysis sessions with analysts from finance. We document observations and recommendations from this study and present evidence on how our approach contribute to the efficiency and productivity of interactive visual analysis sessions involving high-dimensional data.

Published

IEEE Transactions on Visualization and Computer Graphics

  • Volume: PP
  • Number: 99
  • Pages: 1-1
  • ISSN: 1077-2626
  • Date: jan 2017

Media

  • paper
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BibTeX

@article{Turkay-2017-VIS,
  author = C. Turkay and E. Kaya and S. Balcisoy and H. Hauser},
  title = {Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis}, 
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  year = {2017},
  volume = {PP},
  number = {99},
  pages = {1-1}
  month = jan,
  abstract = {In interactive data analysis processes, the dialogue between the human 
   and the computer is the enabling mechanism that can lead to actionable observations 
   about the phenomena being investigated. It is of paramount importance that this 
   dialogue is not interrupted by slow computational mechanisms that do not consider 
   any known temporal human-computer interaction characteristics that prioritize the 
   perceptual and cognitive capabilities of the users. In cases where the analysis 
   involves an integrated computational method, for instance to reduce the dimensionality 
   of the data or to perform clustering, such non-optimal processes are often likely. 
   To remedy this, progressive computations, where results are iteratively improved, 
   are getting increasing interest in visual analytics. In this paper, we present 
   techniques and design considerations to incorporate progressive methods within interactive
   analysis processes that involve high-dimensional data. We define methodologies 
   to facilitate processes that adhere to the perceptual characteristics of users 
   and describe how online algorithms can be incorporated within these. A set of 
   design recommendations and according methods to support analysts in accomplishing 
   high-dimensional data analysis tasks are then presented. Our arguments and 
   decisions here are informed by observations gathered over a series of analysis 
   sessions with analysts from finance. We document observations and recommendations 
   from this study and present evidence on how our approach contribute to the efficiency 
   and productivity of interactive visual analysis sessions involving high-dimensional data.},
  doi={10.1109/TVCG.2016.2598470}, 
  ISSN={1077-2626},   


}






 Last Modified: Jean-Paul Balabanian, 2017-06-30