short-paper

A Question-Oriented Visualization Recommendation Approach for Data Exploration

Authors:

Raul de Araújo Lima,

Simone Diniz Junqueira BarbosaAuthors Info & Claims

AVI '20: Proceedings of the 2020 International Conference on Advanced Visual Interfaces

Article No.: 43, Pages 1 - 5

https://doi.org/10.1145/3399715.3399849

Published: 02 October 2020 Publication History

Abstract

The increasingly rapid growth of data production and the consequent need to explore data to obtain answers to the most varied questions have promoted the development of tools to facilitate the manipulation and construction of data visualizations. However, building useful data visualizations is not a trivial task: it may involve a large number of subtle decisions from experienced designers. In this paper, we present an approach that uses a set of heuristics to recommend data visualizations associated with questions, in order to facilitate the understanding of the recommendations and assisting the visual exploration process. Our approach was implemented and evaluated through the VisMaker tool. We carried out two studies comparing VisMaker with Voyager 2 and analyzed some aspects of the recommendation approaches through the participants' feedbacks. As a result, we found some advantages of our approach and gathered comments to help improve the development of visualization recommender tools.

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Cited By

Rodrigues ABarbosa GLopes HBarbosa SSantos CBento Villela MRios da Hora Rodrigues Kde Gois Ribeiro Darin T(2022)Investigating whether people identify how suitable a data visualization is for answering specific analysis questionsProceedings of the 21st Brazilian Symposium on Human Factors in Computing Systems10.1145/3554364.3560904(1-11)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3554364.3560904
Setlur VTory M(2022)How do you Converse with an Analytical Chatbot? Revisiting Gricean Maxims for Designing Analytical Conversational BehaviorProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501972(1-17)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3501972

Index Terms

A Question-Oriented Visualization Recommendation Approach for Data Exploration
1. Human-centered computing
  1. Visualization

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Behavior-driven visualization recommendation
IUI '09: Proceedings of the 14th international conference on Intelligent user interfaces

We present a novel approach to visualization recommendation that monitors user behavior for implicit signals of user intent to provide more effective recommendation. This is in contrast to previous approaches which are either insensitive to user intent ...
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Visualization techniques are of increasing importance in exploring and analyzing large amounts of multidimensional information. One important class of visualization techniques which is particularly interesting for visualizing very large multidimensional ...

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cover image ACM Other conferences

AVI '20: Proceedings of the 2020 International Conference on Advanced Visual Interfaces

September 2020

613 pages

ISBN:9781450375351

DOI:10.1145/3399715

Copyright © 2020 ACM.

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Published: 02 October 2020

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Short-paper
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AVI '20

AVI '20: International Conference on Advanced Visual Interfaces

September 28 - October 2, 2020

Salerno, Italy

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AVI '20 Paper Acceptance Rate 36 of 123 submissions, 29%;

Overall Acceptance Rate 128 of 490 submissions, 26%

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Cited By

Rodrigues ABarbosa GLopes HBarbosa SSantos CBento Villela MRios da Hora Rodrigues Kde Gois Ribeiro Darin T(2022)Investigating whether people identify how suitable a data visualization is for answering specific analysis questionsProceedings of the 21st Brazilian Symposium on Human Factors in Computing Systems10.1145/3554364.3560904(1-11)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3554364.3560904
Setlur VTory M(2022)How do you Converse with an Analytical Chatbot? Revisiting Gricean Maxims for Designing Analytical Conversational BehaviorProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501972(1-17)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3501972

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