The IEEE Information Visualization conference (“InfoVis”) solicits research papers on a diverse set of topics related to information visualization. Broadly defined, information visualization is the design of visual data representations and interaction techniques that support human activities where the spatial layout of the visual representation is not a direct mapping of spatial relationships in the data. Papers may contribute novel visual encoding or interaction techniques, evaluations of InfoVis techniques and tools, models or theories related to InfoVis, systems that support visual data analysis, or applications of information visualization to domain-specific problems. None of these guidelines are in any way prescriptive; in fact, many successful papers combine two contribution types, and some of the very best papers often combine several.

Please note that topics primarily involving spatial data (such as scalar, vector and tensor fields) might be a better match for the IEEE SciVis Conference at IEEE VIS. Similarly, topics that clearly focus on visual analytics, e.g., computational solutions facilitated by visual interfaces to support analysis, might be a better match for the IEEE VAST Conference, also at IEEE VIS. Papers chairs reserve the right to move papers between conferences based on its topic and perceived fit.

Topics

Research contributions are welcomed across a range of topics including, but not limited to:

Information visualization techniques for

    graphs (networks), trees (hierarchies), and other relational data
    high-dimensional data and dimensionality reduction
    multivariate data
    heterogeneous data
    personal or social data (health, energy, finance, fitness, email, etc.)
    text and documents
    non-numeric data (categorical data, nominal data, etc.)
    non-expert audiences
    causality and uncertainty data
    time-series & temporal event data
    spatial data, particularly visualized with a new spatial mapping
    combinations of abstract and spatial data
    streaming or time-varying data
    very large datasets

Interaction techniques for visualizations or for supporting the data analysis process, including

    recordkeeping, sensemaking, and storytelling
    collaboration support (both co-located and distributed)
    integration of visualization with other software tools
    post-WIMP interactions (pen, touch, speech, gestures, etc.)
    focus + context and overview + detail methods
    zooming, navigation, and distortion techniques
    brushing and linking
    coordinated multiple views
    data labeling, editing, and annotation

Integration of visualizations into the context of use, including

    visual design and aesthetics
    minimal attention contexts (e.g. ambient displays, second screens)
    mobile and ubiquitous applications
    public environments

Information visualization fundamentals and methodologies:

    novel algorithms and mathematics
    taxonomies and models
    research methodology, discussions, and frameworks
    cognition and perception

Evaluation:

    task and requirements analysis
    metrics and benchmarks
    qualitative and quantitative evaluation
    laboratory and field studies
    novel evaluation methods
    usability studies and focus groups
    case studies (involving real users)
    replications of past studies that validate or contradict key findings

Applied information visualization:

    reports of information visualization in domains where it has impact
    using information visualization for education and teaching
    design studies