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  • 1
    Online Resource
    Online Resource
    SetCoLa: High‐Level Constraints for Graph Layout
    Wiley ; 2018
    In:  Computer Graphics Forum Vol. 37, No. 3 ( 2018-06), p. 537-548
    Details
    In: Computer Graphics Forum, Wiley, Vol. 37, No. 3 ( 2018-06), p. 537-548
    Abstract: Constraints enable flexible graph layout by combining the ease of automatic layout with customizations for a particular domain. However, constraint‐based layout often requires many individual constraints defined over specific nodes and node pairs. In addition to the effort of writing and maintaining a large number of similar constraints, such constraints are specific to the particular graph and thus cannot generalize to other graphs in the same domain. To facilitate the specification of customized and generalizable constraint layouts, we contribute SetCoLa: a domain‐specific language for specifying high‐level constraints relative to properties of the backing data. Users identify node sets based on data or graph properties and apply high‐level constraints within each set. Applying constraints to node sets rather than individual nodes reduces specification effort and facilitates reapplication of customized layouts across distinct graphs. We demonstrate the conciseness, generalizability, and expressiveness of SetCoLa on a series of real‐world examples from ecological networks, biological systems, and social networks.
    Type of Medium: Online Resource
    ISSN: 0167-7055 , 1467-8659
    URL: Issue
    URL: Article
    DOI: 10.1111/cgf.2018.37.issue-3
    DOI: 10.1111/cgf.13440
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 1482655-0
    detail.hit.zdb_id: 246488-3
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Characterizing Exploratory Visual Analysis: A Literature Review and Evaluation of Analytic Provenance in Tableau
    Wiley ; 2019
    In:  Computer Graphics Forum Vol. 38, No. 3 ( 2019-06), p. 145-159
    Details
    In: Computer Graphics Forum, Wiley, Vol. 38, No. 3 ( 2019-06), p. 145-159
    Abstract: Supporting exploratory visual analysis (EVA) is a central goal of visualization research, and yet our understanding of the process is arguably vague and piecemeal. We contribute a consistent definition of EVA through review of the relevant literature, and an empirical evaluation of existing assumptions regarding how analysts perform EVA using Tableau, a popular visual analysis tool. We present the results of a study where 27 Tableau users answered various analysis questions across 3 datasets. We measure task performance, identify recurring patterns across participants' analyses, and assess variance from task specificity and dataset. We find striking differences between existing assumptions and the collected data. Participants successfully completed a variety of tasks, with over 80% accuracy across focused tasks with measurably correct answers. The observed cadence of analyses is surprisingly slow compared to popular assumptions from the database community. We find significant overlap in analyses across participants, showing that EVA behaviors can be predictable. Furthermore, we find few structural differences between behavior graphs for open‐ended and more focused exploration tasks.
    Type of Medium: Online Resource
    ISSN: 0167-7055 , 1467-8659
    URL: Issue
    URL: Article
    DOI: 10.1111/cgf.2019.38.issue-3
    DOI: 10.1111/cgf.13678
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1482655-0
    detail.hit.zdb_id: 246488-3
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Animated Transitions in Statistical Data Graphics
    Institute of Electrical and Electronics Engineers (IEEE) ; 2007
    In:  IEEE Transactions on Visualization and Computer Graphics Vol. 13, No. 6 ( 2007-11), p. 1240-1247
    Details
    In: IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers (IEEE), Vol. 13, No. 6 ( 2007-11), p. 1240-1247
    Type of Medium: Online Resource
    ISSN: 1077-2626
    URL: Article
    DOI: 10.1109/TVCG.2007.70539
    RVK:
    SQ 1100
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2007
    detail.hit.zdb_id: 2027333-2
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Beyond Weber's Law: A Second Look at Ranking Visualizations of Correlation
    Institute of Electrical and Electronics Engineers (IEEE) ; 2016
    In:  IEEE Transactions on Visualization and Computer Graphics Vol. 22, No. 1 ( 2016-1-31), p. 469-478
    Details
    In: IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers (IEEE), Vol. 22, No. 1 ( 2016-1-31), p. 469-478
    Type of Medium: Online Resource
    ISSN: 1077-2626
    URL: Article
    DOI: 10.1109/TVCG.2015.2467671
    RVK:
    SQ 1100
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016
    detail.hit.zdb_id: 2027333-2
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    Online Resource
  • 5
    Online Resource
    Online Resource
    Reverse‐Engineering Visualizations: Recovering Visual Encodings from Chart Images
    Wiley ; 2017
    In:  Computer Graphics Forum Vol. 36, No. 3 ( 2017-06), p. 353-363
    Details
    In: Computer Graphics Forum, Wiley, Vol. 36, No. 3 ( 2017-06), p. 353-363
    Abstract: We investigate how to automatically recover visual encodings from a chart image, primarily using inferred text elements. We contribute an end‐to‐end pipeline which takes a bitmap image as input and returns a visual encoding specification as output. We present a text analysis pipeline which detects text elements in a chart, classifies their role (e.g., chart title, x‐axis label, y‐axis title, etc.), and recovers the text content using optical character recognition. We also train a Convolutional Neural Network for mark type classification. Using the identified text elements and graphical mark type, we can then infer the encoding specification of an input chart image. We evaluate our techniques on three chart corpora: a set of automatically labeled charts generated using Vega, charts from the Quartz news website, and charts extracted from academic papers. We demonstrate accurate automatic inference of text elements, mark types, and chart specifications across a variety of input chart types.
    Type of Medium: Online Resource
    ISSN: 0167-7055 , 1467-8659
    URL: Issue
    URL: Article
    DOI: 10.1111/cgf.2017.36.issue-3
    DOI: 10.1111/cgf.13193
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 1482655-0
    detail.hit.zdb_id: 246488-3
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    Online Resource
  • 6
    Online Resource
    Online Resource
    Visual Debugging Techniques for Reactive Data Visualization
    Wiley ; 2016
    In:  Computer Graphics Forum Vol. 35, No. 3 ( 2016-06), p. 271-280
    Details
    In: Computer Graphics Forum, Wiley, Vol. 35, No. 3 ( 2016-06), p. 271-280
    Abstract: Interaction is critical to effective visualization, but can be difficult to author and debug due to dependencies among input events, program state, and visual output. Recent advances leverage reactive semantics to support declarative design and avoid the “spaghetti code” of imperative event handlers. While reactive programming improves many aspects of development, textual specifications still fail to convey the complex runtime dynamics. In response, we contribute a set of visual debugging techniques to reveal the runtime behavior of reactive visualizations. A timeline view records input events and dynamic variable updates, allowing designers to replay and inspect the propagation of values step‐by‐step. On‐demand annotations overlay the output visualization to expose relevant state and scale mappings in‐situ. Dynamic tables visualize how backing datasets change over time. To evaluate the effectiveness of these techniques, we study how first‐time Vega users debug interactions in faulty, unfamiliar specifications; with no prior knowledge, participants were able to accurately trace errors through the specification.
    Type of Medium: Online Resource
    ISSN: 0167-7055 , 1467-8659
    URL: Issue
    URL: Article
    DOI: 10.1111/cgf.2016.35.issue-3
    DOI: 10.1111/cgf.12903
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 1482655-0
    detail.hit.zdb_id: 246488-3
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    Online Resource
  • 7
    Online Resource
    Online Resource
    Scented Widgets: Improving Navigation Cues with Embedded Visualizations
    Institute of Electrical and Electronics Engineers (IEEE) ; 2007
    In:  IEEE Transactions on Visualization and Computer Graphics Vol. 13, No. 6 ( 2007-11), p. 1129-1136
    Details
    In: IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers (IEEE), Vol. 13, No. 6 ( 2007-11), p. 1129-1136
    Type of Medium: Online Resource
    ISSN: 1077-2626
    URL: Article
    DOI: 10.1109/TVCG.2007.70589
    RVK:
    SQ 1100
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2007
    detail.hit.zdb_id: 2027333-2
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    Online Resource
  • 8
    Online Resource
    Online Resource
    Boba: Authoring and Visualizing Multiverse Analyses
    Institute of Electrical and Electronics Engineers (IEEE) ; 2021
    In:  IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2 ( 2021-2), p. 1753-1763
    Details
    In: IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers (IEEE), Vol. 27, No. 2 ( 2021-2), p. 1753-1763
    Type of Medium: Online Resource
    ISSN: 1077-2626 , 1941-0506 , 2160-9306
    URL: Article
    DOI: 10.1109/TVCG.2020.3028985
    RVK:
    SQ 1100
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2021
    detail.hit.zdb_id: 2027333-2
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    Online Resource
  • 9
    Online Resource
    Online Resource
    Enterprise Data Analysis and Visualization: An Interview Study
    Institute of Electrical and Electronics Engineers (IEEE) ; 2012
    In:  IEEE Transactions on Visualization and Computer Graphics Vol. 18, No. 12 ( 2012-12), p. 2917-2926
    Details
    In: IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers (IEEE), Vol. 18, No. 12 ( 2012-12), p. 2917-2926
    Type of Medium: Online Resource
    ISSN: 1077-2626
    URL: Article
    DOI: 10.1109/TVCG.2012.219
    RVK:
    SQ 1100
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2012
    detail.hit.zdb_id: 2027333-2
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    Online Resource
  • 10
    Online Resource
    Online Resource
    Extracting and Retargeting Color Mappings from Bitmap Images of Visualizations
    Institute of Electrical and Electronics Engineers (IEEE) ; 2018
    In:  IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 1 ( 2018-1), p. 637-646
    Details
    In: IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers (IEEE), Vol. 24, No. 1 ( 2018-1), p. 637-646
    Type of Medium: Online Resource
    ISSN: 1077-2626
    URL: Article
    DOI: 10.1109/TVCG.2017.2744320
    RVK:
    SQ 1100
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018
    detail.hit.zdb_id: 2027333-2
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    Online Resource
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