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An Interactive Visualization Tool for Exploring Implicit Relationships in Relational Datasets

Published: 20 October 2023 Publication History

Abstract

Relational datasets capture insights into complex networks of entities and their interconnections. The analysis of such datasets is critical for various domains, from social and biological networks to scientific research. The complexity and interdependencies inherent to relational datasets present significant challenges for analysts aiming to explore and understand such data. These challenges are particularly notable for individuals lacking expertise in data visualization tools and techniques, as well as those without training in deriving complex relations between the entities contained within the dataset. In this paper, we present a visualization tool designed to facilitate this kind of exploration. We illustrate the use of our tool with a dataset on the scientific production of the VINCI symposium.

References

[1]
Marian Dork, Sheelagh Carpendale, and Carey Williamson. 2012. Visualizing explicit and implicit relations of complex information spaces. Information Visualization 11, 1 (2012), 5–21. https://doi.org/10.1177/1473871611425872
[2]
Jian Zhao, Christopher Collins, Fanny Chevalier, and Ravin Balakrishnan. 2013. Interactive Exploration of Implicit and Explicit Relations in Faceted Datasets. IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 2080–2089. https://doi.org/10.1109/TVCG.2013.167

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  1. An Interactive Visualization Tool for Exploring Implicit Relationships in Relational Datasets

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    Published In

    cover image ACM Other conferences
    VINCI '23: Proceedings of the 16th International Symposium on Visual Information Communication and Interaction
    September 2023
    308 pages
    ISBN:9798400707513
    DOI:10.1145/3615522
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 October 2023

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    Author Tags

    1. Relational datasets
    2. implicit relationships
    3. visualization tool.

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    VINCI 2023

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    Overall Acceptance Rate 71 of 193 submissions, 37%

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