extended-abstract

Touch2Annotate: generating better annotations with less human effort on multi-touch interfaces

Authors:

Yang Chen,

Jing Yang,

Scott Barlowe,

Dong H. JeongAuthors Info & Claims

CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems

Pages 3703 - 3708

https://doi.org/10.1145/1753846.1754042

Published: 10 April 2010 Publication History

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Abstract

Annotation is essential for effective visual sense making. For multidimensional data, most existing annotation approaches require users to manually type notes to record the semantic meaning of their findings. They require high effort from multi-touch interface users since these users often experience low typing speeds and high typing errors. To lower the typing effort and improve the quality of the generated annotations, we propose a new approach that semi-automatically generates annotations with rich semantic meanings on multidimensional visualizations. A working prototype of this approach, named Touch2Annotate, has been implemented and used on a tabletop. We present a scenario of using Touch2Annotate to demonstrate its effectiveness.

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Cited By

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Li HWang YQu H(2024)Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI CollaborationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642726(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642726
Sorenson-Graff RBae SWirfs-Brock J(2024)Integrating Annotations for Sonifications and Physicalizations2024 IEEE Visualization and Visual Analytics (VIS)10.1109/VIS55277.2024.00063(276-280)Online publication date: 13-Oct-2024
https://doi.org/10.1109/VIS55277.2024.00063
Lei FMa YFotheringham AMack ELi ZSachdeva MBardin SMaciejewski R(2024)GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report GenerationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332735930:1(1391-1401)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327359
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Index Terms

Touch2Annotate: generating better annotations with less human effort on multi-touch interfaces
1. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

CHI EA '10: CHI '10 Extended Abstracts on Human Factors in Computing Systems

April 2010

2219 pages

ISBN:9781605589305

DOI:10.1145/1753846

General Chairs:
Elizabeth Mynatt
Georgia Institute of Technology
,
Don Schoner
Georgia Institute of Technology
,
Program Chairs:
Geraldine Fitzpatrick
Vienna University of Technology
,
Scott Hudson
Carnegie Mellon University
,
Keith Edwards
Georgia Tech
,
Tom Rodden
University of Nottingham

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Association for Computing Machinery

New York, NY, United States

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Published: 10 April 2010

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CHI '10: CHI Conference on Human Factors in Computing Systems

April 10 - 15, 2010

Georgia, Atlanta, USA

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CHI EA '10 Paper Acceptance Rate 350 of 1,346 submissions, 26%;

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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Li HWang YQu H(2024)Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI CollaborationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642726(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642726
Sorenson-Graff RBae SWirfs-Brock J(2024)Integrating Annotations for Sonifications and Physicalizations2024 IEEE Visualization and Visual Analytics (VIS)10.1109/VIS55277.2024.00063(276-280)Online publication date: 13-Oct-2024
https://doi.org/10.1109/VIS55277.2024.00063
Lei FMa YFotheringham AMack ELi ZSachdeva MBardin SMaciejewski R(2024)GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report GenerationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332735930:1(1391-1401)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327359
Chen QCao SWang JCao N(2024)How Does Automation Shape the Process of Narrative Visualization: A Survey of ToolsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.326132030:8(4429-4448)Online publication date: Aug-2024
https://doi.org/10.1109/TVCG.2023.3261320
Shen LShen ELuo YYang XHu XZhang XTai ZWang J(2023)Towards Natural Language Interfaces for Data Visualization: A SurveyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.314800729:6(3121-3144)Online publication date: 1-Jun-2023
https://doi.org/10.1109/TVCG.2022.3148007
Lai CLin ZJiang RHan YLiu CYuan XBernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)Automatic Annotation Synchronizing with Textual Description for VisualizationProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376443(1-13)Online publication date: 21-Apr-2020
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Zhu SSun GJiang QZha MLiang R(2020)A survey on automatic infographics and visualization recommendationsVisual Informatics10.1016/j.visinf.2020.07.002Online publication date: Aug-2020
https://doi.org/10.1016/j.visinf.2020.07.002
Ren DBrehmer MBongshin Lee Hollerer TChoe E(2017)ChartAccent: Annotation for data-driven storytelling2017 IEEE Pacific Visualization Symposium (PacificVis)10.1109/PACIFICVIS.2017.8031599(230-239)Online publication date: Apr-2017
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Lammel FManssour ISilveira M(2016)Multi-touch Interaction with Information Visualization Techniques: An Analysis Using Twitter DataHuman-Computer Interaction. Novel User Experiences10.1007/978-3-319-39513-5_20(211-222)Online publication date: 19-Jun-2016
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Lee EGupta ADarvill DDill JShaw CWoodbury R(2014)The CZSaw notes case studyVisualization and Data Analysis 201410.1117/12.2041318(901706)Online publication date: 3-Feb-2014
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Contextifier: automatic generation of annotated stock visualizations

Semantic Annotating of Text Documents: Basic Concepts and Taxonomic Approach

Comparison of Methods to Annotate Named Entity Corpora

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