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Snap-together visualization: a user interface for coordinating visualizations via relational schemata

Authors:

Ben ShneidermanAuthors Info & Claims

AVI '00: Proceedings of the working conference on Advanced visual interfaces

Pages 128 - 135

https://doi.org/10.1145/345513.345282

Published: 01 May 2000 Publication History

Abstract

Multiple coordinated visualizations enable users to rapidly explore complex information. However, users often need unforeseen combinations of coordinated visualizations that are appropriate for their data. Snap-Together Visualization enables data users to rapidly and dynamically mix and match visualizations and coordinations to construct custom exploration interfaces without programming. Snap's conceptual model is based on the relational database model. Users load relations into visualizations then coordinate them based on the relational joins between them. Users can create different types of coordinations such as: brushing, drill down, overview and detail view, and synchronized scrolling. Visualization developers can make their independent visualizations snap-able with a simple API.

Evaluation of Snap revealed benefits, cognitive issues, and usability concerns. Data savvy users were very capable and thrilled to rapidly construct powerful coordinated visualizations. A snapped overview and detail-view coordination improved user performance by 30-80%, depending on task.

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https://doi.org/10.2196/58088
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Snap-together visualization: a user interface for coordinating visualizations via relational schemata
1. Human-centered computing

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Published In

cover image ACM Conferences

AVI '00: Proceedings of the working conference on Advanced visual interfaces

May 2000

317 pages

ISBN:1581132522

DOI:10.1145/345513

Chairmen:
Stefano Levialdi
Univ. di Roma “La Sapienza”, Roma, Italy
,
Vita Di Gesù
Univ. di Patermo, Patermo, Italy
,
Laura Tarantino
Univ. degli Studi dell'Aquila, L'Aquila, Italy

Copyright © 2000 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
University of L'Aquila: University of L'Aquila

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 May 2000

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AVI00: Advanced Visual Interfaces

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Cited By

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https://doi.org/10.2196/58088
Heer JMoritz D(2024)Mosaic: An Architecture for Scalable & Interoperable Data ViewsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332718930:1(436-446)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327189
Snyder LHeer J(2024)DIVI: Dynamically Interactive VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332717230:1(403-413)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327172
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Zeng WChen XHou YShao LChu ZChang R(2024)Semi-Automatic Layout Adaptation for Responsive Multiple-View Visualization DesignIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.324035630:7(3798-3812)Online publication date: Jul-2024
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Cibulski LMay TSchmidt JKohlhammer J(2023)COMPO*SED: Composite Parallel Coordinates for Co-Dependent Multi-Attribute ChoicesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.318089929:10(4047-4061)Online publication date: 1-Oct-2023
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Roberts JButcher PRitsos P(2022)One View Is Not Enough: Review of and Encouragement for Multiple and Alternative Representations in 3D and Immersive VisualisationComputers10.3390/computers1102002011:2(20)Online publication date: 3-Feb-2022
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Huang ZWu EIves ZBonifati AEl Abbadi A(2022)Reptile: Aggregation-level Explanations for Hierarchical DataProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517854(399-413)Online publication date: 10-Jun-2022
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