The Human User in Progressive Visual Analytics (Unknown language)

In: EuroVis 2019 - Short Papers   ;  19-23  ;  2019

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The amount of generated and analyzed data is ever increasing, and processing such large data sets can take too long in situations where time-to-decision or fluid data exploration are critical. Progressive visual analytics (PVA) has recently emerged as a potential solution that allows users to analyze intermediary results during the computation without waiting for the computation to complete. However, there has been limited consideration on how these techniques impact the user. Based on discussions from a Dagstuhl seminar held in October 2018, this paper characterizes PVA users by their common roles, their main tasks, and their distinct focus of analysis. It further discusses cognitive biases that play a particular role in PVA. This work will help PVA visualization designers in devising systems that are tailored for their specific target users and their characteristics.

Table of contents conference proceedings

The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

1
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7
The Impact of Distribution and Chart Type on Part-to-Whole Comparisons
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13
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19
The Human User in Progressive Visual Analytics
Micallef, Luana / Schulz, Hans-Jörg / Angelini, Marco / Aupetit, Michaël / Chang, Remco / Kohlhammer, Jörn / Perer, Adam / Santucci, Giuseppe | 2019
25
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31
Color Names Across Languages: Salient Colors and Term Translation in Multilingual Color Naming Models
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37
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43
ReLVis: Visual Analytics for Situational Awareness During Reinforcement Learning Experimentation
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49
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55
Highlight Insert Colormaps: Luminance for Focused Data Analysis
Samsel, Francesca / Overmyer, Trinity / Navrátil, Paul A. | 2019
61
Simulation and Visualization of Fluid Flows Around Real Objects in Augmented Reality
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67
Voronoi-Based Foveated Volume Rendering
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73
Defining an Analysis: A Study of Client-Facing Data Scientists
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79
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85
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91
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97
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103
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109
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115
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121
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127
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