Conference Information

InfoVis 2017: IEEE Information Visualization Conference

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Submission Date:
2017-03-21
Notification Date:
2017-06-06
Conference Date:
2017-10-01
Location:
Phoenix, Arizona, USA
Viewed: 47591   Tracked: 3   Attend: 0

Conference Partner Index (CP-I)

53.5 / 100
Ranked #954 of 5,655 conferences · Top 17%

#39 of 148 in Computer Graphics & Multimedia

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
81
Community attention (10%)
24
Public record completeness (15%)
50

Inputs used: Editions on record: 19 · Researchers following it here: 3 · Researchers who opened this page in the past 24 months: 3

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-09

Call For Papers

InfoVis 2017 (IEEE Information Visualization Conference) is an academic conference held in Phoenix, Arizona, USA on 2017-10-01. The paper submission deadline is 2017-03-21. Acceptance notifications are sent on 2017-06-06.

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
Last updated by Dou Sun on

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