VIS Paper Submission Keywords

At VIS 2019, the VEC and the V-I-S Steering Committees adopted a proposal by the reVISe committee to replace the current PCS keywords with a new set of keywords to improve the reviewing process.

The motivation for this change, as well as a preliminary brief overview of the new keywords can be found in the reVISe Amended Proposal.

An more comprehensive explanatory set of pages is currently under preparation. Please check back when the PCS submissions open.

The following are the categories of keywords and their descriptions:

Data Types and Their Use in Visualization and Visual Analytics

  • Geospatial Data (Geospatial): data with geospatial (lat/lon) locations or trajectories
  • Graph/Network and Tree Data (Network): data with network (node/link) or tree/hierarchy structure
  • High-dimensional Data (HD): data with a large number of dimension columns (features/attributes) that requires extra effort to process
  • Data Models (Models): the structure of statistical and simulation models, model results and outputs, and the parameter spaces of model inputs as for example in machine learning
  • Scalar Field Data (Scalar): spatial/volume data with one or more scalar variables
  • Image and Video Data (ImageVideo): imagery data in the form of stills or video
  • Tabular Data (Tabular): tables of row/column data with a moderate number of columns that are directly represented
  • Temporal Data (Time): data that has a temporal component (e.g. time series, time-oriented data, events, time-varying data, trajectories over time…)
  • Text/Document Data (Text): data in the form of text or documents
  • Vector and Tensor Field Data (Vector_Tensor): spatial data containing vector and tensor fields
  • Other Data (OtherData): a data type that does not reasonably fit into any other category
  • DataType Agnostic (NAData): no special expertise on data types is required for my paper

Intended Contributions to Visualization and Visual Analytics

General Contributions

  • Algorithms (Algorithm): the design or implementation of data analysis/visualization algorithms
  • Data Abstractions and Types (DataAbstr): the process of reducing a particular body of data to a simplified representation and/or improvements or new uses of datasets/-types
  • Datasets (Datasets): contributing new datasets for benchmarking or understanding techniques / the field itself
  • Deployment (Deployment): deployment of tools/techniques “in the wild”
  • Methodologies (Methodology): methodologies for visualization incl. design, evaluation, processes, collaboration, …
  • Application Motivated Visualization (Application): applying, adapting, or creating novel visualization techniques to address specific challenges presented by real-world applications; incl. design studies
  • Guidelines (Guidelines): deriving or applying guidelines for design and use of visualization & visual analytics techniques
  • Interaction Design (Interaction): the design of interaction techniques and/or interaction design methodologies and practices for any interaction modalities (touch, pen, mouse, speech, proxemics, …)
  • Process/Workflow Design (Workflow): designing, developing, evaluating, or improving data analysis workflows Systems Software Architecture, Toolkit/Library, Language …designing/implementing novel platforms/libraries/toolkits for developing or testing
  • Software Prototype (Software): writing or analyzing concrete implementations of tools / systems / applications
  • State-of-the-art Survey (STAR): conducting, structuring, and writing systematic literature reviews
  • Task Abstractions & Application Domains (Domain_Task): the practice of eliciting domain or task abstractions and challenges from specific application domains
  • Taxonomy, Models, Frameworks, Theory (Theory): deriving systematic characterizations of a particular space (e.g. design space, taxonomy of techniques), novel abstractions of concepts, discussions of formalisms
  • Visual Representation Design (VisDesign): designing data visualization / visual representations and/or practices/processes of visualization design
  • Other Contribution (OtherContrib): a contribution type that does not reasonabily fit in any other category

    Evaluation Contributions

  • Computational Benchmark Studies (CompBenchmark): design/conducting/analysis of computational benchmark studies that for example compare performance results from running implemented techniques/algorithms
  • Human-Subjects Qualitative Studies (HumanQual): design/conducting/analysis of qualitative empirical studies involving human participants
  • Human-Subjects Quantitative Studies (HumanQuant): design/conducting/analysis of quantitative empirical studies involving human participants

Application Areas for Visualization and Visual Analytics

  • Computing: Software, Networks, Security, Performance Engr., Distr. Systems, Databases (CompSystems): applications to the general computing domain incl. software, networks, security, databases, visualization (Vis4Vis) etc.
  • Life Sciences, Health, Medicine, Biology, Bioinformatics, Genomics (LifeBio): applications to the life sciences: incl. medicine, biology, bioinformatics, genomics, health informatics, or others
  • Machine Learning, Statistics, Modelling, and Simulation Applications (MLStatsModel): applications to machine learning, statistics, modelling, or simulation applications (note: find ML for VIS under “Techniques” below)
  • Physical & Environmental Sciences, Engineering, Mathematics (ScienceEngr): applications to physical & environmental sciences, engineering, or mathematics
  • Social Science, Education, Humanities, Journalism, Intelligence Analysis, Knowledge Work (SocHum): applications to the social sciences, education, and humanities incl. knowledge work such as intelligence analysis
  • Other Application Areas (OtherApp): applications to an application area that does not reasonably fit in any other category
  • Domain Agnostic (NAApp): no special expertise on application areas is required for my paper

Human Factors

  • Collaboration (Collab): collaborative data analysis, collaborative workflows, and theories of collaboration
  • Color (Color): the use of color in visualization
  • Communication/Presentation, Storytelling (Storytelling): using visualization to communicate or present a narrative or story from data
  • Data Analysis, Reasoning, Problem Solving, and Decision Making (AnalyzeDecide): support of analytical reasoning, problem solving, decision-making, analysis workflows, and other related cognitive processes
  • General Public (GenPublic): the design and dissemination of tools for/to the general public / communication to the general public or mass audiences
  • Mixed Initiative Human-Machine Analysis (MixedInit): balancing computational and human effort for data analysis
  • Perception & Cognition (Perception): perception and cognition
  • Personal Visualization, Personal Visual Analytics (PersonalVis): design of interactive visual representations for use in a personal context; analytical reasoning by visual representations for use in a personal context

    Stats & Math, Machine Learning, Data Management Methods & Algorithms

  • Data Clustering and Aggregation (ClusterAgg): algorithmic and visualization approaches for aggregating or clustering data
  • Data Management, Processing, Wrangling (DataMgmt): steps for cleaning, processing, and managing data
  • Dimensionality Reduction (DimRed): the use of / techniques for reducing the number of variables under consideration
  • Feature Detection, Extraction, Tracking & Transformation (Features): methods for finding, detecting, mining, extracting, retrieving, transforming, discovering and tracking data, features, patterns, knowledge
  • Large-Scale Data Techniques (BigData): techniques specific to handling large amounts of data
  • Machine Learning Techniques (ML): the use of machine learning in visualization / visual analytics
  • Mathematical Foundations & Numerical Methods (Math): mathematical foundations and numerical methods and their use

    Spatial Field Methods & Algorithms

  • Computational Topology-based Techniques (CompTop): computational topology and/or topological abstractions and their use
  • Isosurface Techniques (Isosurfaces): extraction and use of isosurfaces and generalizations
  • Vector, Tensor & Flow Visualization (Flow): techniques for vector fields, tensor flow, tractography, and fluid mechanics
  • Volume Rendering (Volumes): rendering techniques and algorithms for direct visualization of volumetric data

    General Visualization Methods

  • Animation and Motion-related Techniques (Motion): methods using animation or other forms for the display of motion
  • Art & Graphic Design (Art): data art, art practice, art-science collaboration, graphic design practice, …
  • Cartography, Maps (Maps): design and use of maps and mapping technology
  • Charts, Diagrams, and Plots (Charts): statistical graphics such as charts, diagrams, or plots (line/bar charts, etc.)
  • Comparison and Similarity (Comparison): methods for visual comparison or determining similarity
  • Computer Graphics Techniques (Graphics): techniques from the field of computer graphics, including raycasting, illustrative / non-photorealistic rendering, etc.
  • Coordinated and Multiple Views (MultiView): linked views, multiple views, coordinated views, coordinated multiple views, or coupled views
  • Image and Signal Processing (ImageProcessing): image and signal processing methods
  • Mobile, AR/VR/Immersive, Specialized Input/Display Hardware (Displays): specialized interaction and display techniques and hardware (mobile, caves, heads-up displays, physicalization, tangibles,… and combinations of devices)
  • Multi-Resolution and Level of Detail Techniques (MultiRes): visualization techniques for showing multiple levels of detail and resolution, including focus+context
  • Specific Computing and Rendering Hardware (Hardware): how to use specific computing or rendering hardware for visualization (CPU/GPU clusters, etc)
  • Uncertainty Visualization (Uncertainty): visually communicating uncertainty (of data, models, algorithmic results, or the visualization process)
  • OtherTopic: a visualization/visual analytics related topic/technique that does not reasonably fit in any category above