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