Area Model for VIS

Introduction

Ever since VIS 2021, the VIS conference has used an area model grouping shared research interests for purposes of peer review. The current area model groups different research topics in visualization and visual analytics into six areas. This allows research papers on closely-related topics to be reviewed in a coherent manner. To ensure a high-quality review process, two or more area paper chairs oversee the reviewing process for each area and draw program committee members from a large joint program committee (PC).

This page provides guidance on how the area model affects authors, reviewers, paper chairs, and the reviewing process more generally. It also gives guidance on how to make an appropriate choice during paper submission.

How Does the Area Model Affect Me?

VIS Areas and Paper Authors

As an author, your main task is to choose an area for your submission. You should think of areas mostly as logistical divisions that ensure a high-quality reviewing process. First, try to find an area for your paper based on the topics grouped in the area. Secondly, if your paper could go into multiple areas, look at the area paper chairs and their expertise. Which area has the area paper chairs with expertise related to your paper? Note that program committee members are not exclusive to an area, so you do not have to worry about identifying which PC members would be suitable to review your manuscript. There are two exceptions to the general recommendations above: There are two exceptions to the general recommendations above:

  • Do not submit to an area if you or any of your co-authors are in conflict with all area paper chairs.
  • If you or one of your co-authors is an area paper chair, you cannot submit to your own area.

The FAQ below answers further specific questions about choosing areas.

VIS Areas and Paper Chairs

The area model has two types of paper chairs: Overall Paper Chairs (OPCs) and Area Paper Chairs (APCs). Area paper chairs have many of the responsibilities related to the reviewing process: assigning program committee members to submissions, recommending a paper for acceptance, and nominating a paper for awards. The overall paper chairs are there to ensure a coherent reviewing process across all areas, help to resolve conflicts, guide the area paper chairs, and make cross-area decisions. Area chairs cannot submit to their own area.

VIS Areas and Reviewers

Each IEEE VIS paper will be assigned two members from the program committee as well as one external reviewer. External reviewers take part in the process as usual in any conference reviewing system.. Program committee members bid across all papers submitted to IEEE VIS. To ease bidding, mechanisms including updated keywords have been put in place in the conference submission system (PCS) to ensure that it remains manageable.

The VIS Area Model

The use of data visualization can be traced back to at least a millennium ago. The emergence of a variety of graphical plots in the 1800s introduced statistical graphics as a sub-area of statistics. The 1987 NSF report entitled “Visualization in Scientific Computing” prepared the community for the first IEEE Visualization Conference in 1990. Since then, visualization has become a scientific field, and has expanded to encompass several significant focal points, namely scientific visualization, information visualization and visual analytics as well as many domain-specific areas, such as geo-information visualization, biological and medical data visualization, software visualization, and others. This event series has provided the field of visualization with a prestigious and broad international platform. The early unification effort resulted in the changes of its name to IEEE VisWeek in 2008 and IEEE VIS in 2013. In 2018, the VIS community started the reVISe restructuring process to address the needs for unification and cohesion while maintaining its vibrancy and growth. The area model described here is the result of this reVISe process.

Description of VIS Areas

Area 1: Theoretical & Empirical

This area focuses on theoretical and empirical research topics that aim to establish the foundation of visualization as a scientific subject. As such, work submitted to this area may also relate closely to the topics covered in other areas. Work submitted to this area should contribute either theoretical or empirical research.

Theoretical Work

Theoretical work which aims to contribute to fundamental questions that relate to how we understand, assess, categorize, or formalize visualizations and/or visual analytics work. Topics of interest include:

  • Concept Formulation: surveys with organization, synthesis, and reflection; taxonomies and ontologies; guidelines and principles; lexica, syntaxes (grammars), semantics, pragmatics of visualization; and information security, privacy, ethics and professionalism in visualization.
  • Model Development: conceptual models and simulation models for describing aspects of visualization processes (e.g., color perception, knowledge acquisition, collaborative decision making, etc.).
  • Mathematical Formalization: mathematical frameworks, quality metrics, theorems (i.e., mathematically-defined causal relations in visualization).

Empirical Research

Empirical research aims to contribute research methodologies, understand humans or human behavior, or contribute concrete results of assessments of a visualization / visual analytics contribution or its context of use. Topics of interest include:

  • Research Methodology: general methodologies for conducting visualization research, e.g., typology, grounded theory, empirical studies, design studies, task analysis, user engagement, qualitative and quantitative research, etc.
  • Empirical Studies: controlled (e.g., typical laboratory experiments), semi-controlled (e.g., typical crowdsourcing studies), and uncontrolled studies (e.g., small group discussions, think aloud exercises, field observation, ethnographic studies, etc.), which may be in the forms of qualitative or quantitative research and which may be further categorized according to their objectives as follows:
  • Empirical Studies for Evaluation: studies for assessing the effectiveness and usability of specific visualization techniques, tools, systems, and workflows, for collecting lessons learned from failures, and for establishing the best practice.
  • Empirical Studies for Observation, Data Acquisition, and Hypothesis Formulation: studies for observing phenomena in visualization processes, stimulating hypothesis formulation, and collecting data to inform computational models and quality metrics.
  • Empirical Studies for Understanding and Theory Validation: studies for understanding the human factors in visualization processes, including perceptual factors (e.g., visual and nonvisual sensory processes, perception, attention, etc.),cognitive factors (e.g., memory, learning, reasoning, decision-making, problem-solving, knowledge, emotion, etc.), and human behavior (e.g., analytic or collaborative practices).

Example Papers:

Area 2: Applications

This area encompasses all forms of application-focused research, aiming to address real-world challenges through data visualization and visual computing. Research in this area may solve an application-motivated technical problem, formulate best practices in collaborating with domain experts to transform general-purpose data visualization technologies to domain-specific solutions, design and develop data visualization systems and visual analytics workflows for supporting individual applications, or investigate how to adapt and optimize data visualization technologies to support the users in a particular application domain. The technical solutions reported in this area are typically application-specific and often developed in collaboration with domain experts. These solutions can take different forms, such as designs of novel visual representations and interaction techniques, new algorithms and techniques for data transformation, prototypes of data visualization hardware and software, specifications of workflows and best practice, or comprehensive design studies. Application papers underline the impact and importance of data visualization research, demonstrating its relevance beyond the VIS research community.

Topics of interest include:

  • Application Domains: Submissions are welcome across all domains, including fields where visualization is well-established, emerging areas where it is becoming an essential component of workflows, and non-traditional disciplines where the data visualization is rarely explored. Examples span science, healthcare, engineering, business, social sciences, public policy, and more.
  • Application-specific Technical Solutions: Including novel visual representations, interaction techniques, algorithms, techniques, hardware and software prototypes, integrated workflows, recommended working practices, etc. tailored to specific domains.
  • Insight Documentation: Reports of success stories and failures in applying visualization technology in practice, lessons and achievements from multidisciplinary research collaborations, benefits gained from collaboration with domain experts, and guidelines for effectively integrating visualization into domain-specific workflows.
  • Emerging Trends and Interdisciplinary Insights: Studies exploring new opportunities for visualization in cutting-edge domains, integration with AI/ML, human-computer interaction innovations, and insights that may inspire novel applications across multiple fields.

Example Papers:

Area 3: Systems & Rendering

This area focuses on the themes of building systems, algorithms for rendering, and alternate input and output modalities. Papers submitted to this area may present new visualization system architectures, support different computing platforms and development environments, or exploit commodity and specialized hardware devices for either rendering or interaction modalities beyond the desktop. The rendering theme includes algorithms and techniques both in software and through hardware acceleration, and also algorithms for graph layout and label placement.

Topics of interest include:

  • Computing Platforms: commodity hardware, GPU, HPC, energy efficient visualization algorithms and hardware, etc.
  • Visualization and interactive data exploration in Visualization Environments: non-immersive and immersive environments, desktop, mobile, smartwatch, web-based, VR/MR/AR, dome theaters, CAVEs, physicalization, remote collaboration, immersive analytics setups, etc.
  • Display Hardware and Output Devices: large and small displays, stereo displays, volumetric displays, 2D/3D printing, non-visual devices, etc.
  • Interaction Modalities: touch, pen, speech, natural language, gesture, haptics, etc.
  • Development Environments: programming languages, software libraries, authoring systems, visualization toolkits, software frameworks for integration and interoperability, etc.
  • Processing Paradigms: parallel, distributed, out-of-core, progressive, streaming, in situ, in transit, etc.
  • Engineering Visualization Systems: visualization system lifecycle, testing, performance analysis, verification, validation, etc.
  • Visualization Systems: general-purpose and application-specific plug-ins, apps, tools, systems, multi-system workflows, etc.
  • Data and Software Resources: open data, open source software, benchmark data, reproducibility, authentication, etc.
  • Rendering Techniques: surface rendering, volume rendering, point-cloud rendering, line-cloud rendering, global illumination, stylized rendering, transfer functions, etc.
  • Lighting and Shading Models: volume rendering integrals, spectral rendering, learning lighting and shading models from real-world data.
  • Placement Techniques: object placement, graph layout, etc.
  • Other Synthesis Techniques: fabrication, data physicalization, sonification, haptic feedback, etc.

Example Papers:

Area 4: Representations & Interaction

This area focuses on the design of visual representations and interaction techniques for different types of data, users, and visualization tasks. The data concerned can be of any data types; the users from any user groups and of any level of visualization literacy and skills; and the user tasks can be of any operational needs. The representations concerned can be of elementary encoding (e.g., visual channels, statistical graphics) as well as complex visual mapping (e.g., spatiotemporal data visualization and coordinated multiple views), and can be in visual as well as non-visual forms. The interaction techniques can be based on traditional WIMP (windows, icons, menus, and pointers), direct manipulation, touch, gesture, natural language, voice, or any other (multimodal) input modality. Papers submitted to this area are normally expected to emphasize their novel contributions in terms of the design of visual representations and interaction techniques, while the work may also discuss the related hardware and software components for data transformation, image synthesis and displays, and immersion (see also Areas 3 and 5).

Topics of interests include:

  • Encoding Channels: geometric channels (e.g., location, size, orientation, shape, etc.), optical channels (e.g., color, opacity, shading, motion, etc.), topological and relational channels (e.g., connection, overlapping, etc.), and semantic channels (e.g., number, text, glyph, etc.).
  • Visual, Haptic, or Sonified Data Representations: for textual data, tabular data, relational data (e.g., hierarchy, tree, set, graph/network), geospatial data, temporal data, imagery data, geometric data (mesh-, point-, line-, curve-based data), field-based data (e.g., volumetric, vector, and tensor field), corpus data, multi-type data, uncertain and missing data, models, functions, and procedures (e.g., algorithms and software), etc. in raw, filtered, or transformed (e.g., aggregated) form.
  • Interaction Techniques: UI design for visualization, zoom and navigation, focus+context, magic lens, dynamic queries, direct manipulation, interactive deformation, natural interaction, natural language, touch, gesture, voice, user-adaptive interaction, interoperation between interaction and visualization tasks, editing tools, collaborative visualization, etc.
  • Visual Communication Techniques: illustrative and explanatory visualization, stylized visual representations, storytelling and narrative visualization, textual annotation for visualization, etc.
  • Intelligent Visualization and Interaction: Automated visualization generation, mixed-initiative interaction, learning UI models for automated capabilities in visualization systems.
  • Technical Discourses on Representation and Interaction: visual and interactional metaphors, scalability of visual mapping, interaction costs, 2D vs. 3D representations, static vs. animated representations, visualization literacy, etc.
  • Situated Visualization: representation and interaction techniques in virtual and extended reality, embedded representations, situated and immersive analytics
  • Accessibility in or of Visualization(s): hardware, software and frameworks for accessible visualizations.
  • Human-Data Interaction: techniques, software, and frameworks to enable people to think with, use, and interact with data, including both visual and non-visual information representations.
  • Data representation and exploration based on senses other than vision: e.g., data sonification, haptics, taste, etc.

Example Papers:

Area 5: Data Transformations

This area focuses on the algorithms and techniques that transform data from one form to another to enable effective and efficient visual mapping as required by the intended visual representations. In principle, the source and destination data can be of any data types, such as spatial or non-spatial; continue or discrete; statistical, temporal or streaming; numerical, textual or imagery, etc. Such data transformation, sometimes be referred to as wrangling or munging, encompasses a range of operations, including extracting relevant information from the source data (e.g., surface extraction from volume data, and network construction from textual data), integrating data from different sources (e.g., multi-modality registration), reorganizing data for efficient processing (e.g., hierarchical data representations), enriching data with additional information and functions (e.g., uncertainty analysis and label generation), and improving data quality and usability (e.g., data cleansing). Papers submitted to this area are normally expected to emphasize their novel contributions in terms of the algorithms and techniques for data transformation, while the work may also discuss the intended visual representations and their generation (see also Areas 3 and 4).

Topics of of interests include:

  • Information Extraction and Data Abstraction: keyword extraction, metadata extraction, surface extraction, feature extraction, pattern recognition, structural and semantic analysis, skeletonization, spatial abstraction, topological abstraction, temporal feature tracking, multi-material interfaces, etc.
  • Data Integration: multi-modality, multi-stage, and multi-level data registration, spatial and non-spatial data integration, multi-field representations, etc.
  • Data Reorganization: voxelization, triangularization, multi-resolution sampling and representations (e.g., discrete sampling, volumetric lattices, wavelet representations), spatial partitioning (e.g., octree, k-d tree, bounding volume), data segmentation, compressed data representations, frequency-domain representations, databases for query-based visualization, etc.
  • Data Enrichment: uncertainty analysis, deformable models, label generation, spatialization, etc.
  • Data Wrangling and Improvement: data wrangling, data re-shaping, data cleaning, data editing, data smoothing, and data modelling.
  • Mathematical Frameworks for Data Transformation: numerical analysis, computational geometry, topological analysis, graph theory, statistical analysis, probability theory, information theory, dimensionality reduction, etc.
  • Machine Learning for Data Transformation: automated discovery of data models and data transformation algorithms for visualization, learning-based parameter optimization of data models and data transformation algorithms for visualization, etc.
  • Technical Discourses on Data Processing and Management in Visualization: feature specification, data provenance, processing provenance, interactive processing, data synthesis, quality assurance, etc.

Example Papers:

Area 6: Analytics & Decisions

This area focuses on the design and optimization of integrated human-machine workflows for visual data analysis, knowledge discovery, decision support, machine learning, and other data intelligence tasks. Papers in this area typically address technical problems that cannot be solved using machine-centric (e.g., statistics and algorithms) or human-centric (e.g., visualization and interaction) methods alone. Submissions may tackle one or more of the following challenges: (a) Designing workflows where visualization, interaction, and algorithms jointly support insight, decision-making, or sensemaking. (b) Using interactive visualization to improve trust, interpretability, understanding of machine-centric processes and their underlying models. (c) Developing knowledge-driven and mixed-initiative analytics that draw on theoretical models and empirical findings in cognition, such as distributed, embodied, and enactive cognition, to better support human decision-making. Because these problems inherently involve interactions between humans and computational methods, papers submitted to this area are typically expected to feature a human-machine approach. This area includes visualizations and visual analytic tools that represent these models, or leverage them for producing the visualization.

Topics of interest include:

  • Information Seeking, Knowledge Discovery, and Decision Making: This category includes workflows that help users explore data, identify patterns, generate insights, and make decisions. Technical problems include:
    • information retrieval, multivariate and semantic search;
    • classification, pattern recognition and clustering;
    • similarity, correlation and causality analysis;
    • spatiotemporal tracking and movement analysis;
    • event and sequence analysis;
    • multimedia data analysis;
    • anomaly and change detection;
    • relationship, association, hierarchy, network and structure analysis;
    • intention and behavior analysis;
    • factor analysis and dimensionality reduction; and
    • uncertainty and risk analysis.
  • Visualization-Driven Model Development, Evaluation, and Oversight: This category focuses on visualization and interaction to support the creation, evaluation, and use of machine-learning models. Technical problems include:
    • cleaning and labelling training data;
    • assisting active learning or other semi-automated learning methods;
    • model testing, evaluation and model comparison;
    • analysis of learned models and learning processes;
    • model understanding, explanation, refinement, and steering; and
    • monitoring deployed machine-learned models or other machine-centric processes.
  • Workflow Design, Evaluation, and Optimization: This category covers techniques and best practices that improve the design, development, evaluation, or performance of * integrated data-intelligence workflows. Topics include:
    • techniques, workflow design patterns, and methodological frameworks;
    • evaluation strategies from complex or mixed-initiative workflows; and
    • analyzing and mitigating data, model, and human biases.
  • Guidance, Provenance, and Cognitive Support for Visual Data Analysis: This category focuses on incorporating human expertise, contextual knowledge, or cognitive guidance. Typical contributions include:
    • knowledge acquisition, representation, and reuse;
    • mixed-initiative analytics, real-time guidance and recommendation;
    • provenance management and utilization, post-action review; and
    • knowledge sharing, and analyst training in visual data analysis.
  • Immersive, Situated, and Embodied Analytics: This category focuses on leveraging immersive technologies, situated data representations, or embodied interaction for support exploration, sensemaking, and decision-making, extending traditional visual analytics beyond the desktop. Topics include:
    • virtual, augmented, or mixed reality for data analysis;
    • situational analytics or in-situ overlays;
    • embodied, physical, or context-aware analytics; and
    • evaluation methods for immersive or situated analytic workflows.

Example Papers:

Frequently Asked Questions

  1. My paper fits equally well into two areas – which one should I pick?
    Pick the area where the area paper chairs are likely the most knowledgeable about your paper. Note that program committee members are not specific to the area and can be chosen by any area paper chair.
  2. What happens if I pick the “wrong” area?
    Due to the unified program committee, it is unlikely that your choice would have a strong negative effect on the choice of reviewers and the quality of reviews for your paper. If the area paper chairs feel strongly that your paper is in the wrong area (or are all conflicted with your paper), they can, in exceptional cases, liaise with the chairs of other areas to propose a move to the authors.
  3. Can I tell who reviews in which area?
    No, the program committee is unified, and PC members are likely to review papers from multiple areas.
  4. My submission does not fit into any area – what should I do?
    Closely review the descriptions of the areas. We expect that all papers within the scope of VIS will be able to find one or multiple areas that are suitable to handle a submission. If you believe that your manuscript is in scope of VIS, but does not fit into any area, please contact the overall paper chairs.
  5. How do areas and keywords relate?
    Keywords are an important instrument to match your submitted manuscript to qualified program committee members, who will indicate interest in papers through a bidding process, be assigned by the area paper chairs to papers that are a good match, then invite competent external reviewers, and who will also write a review themselves. Areas are administrative divisions, what matters most is that you will have area paper chairs that can correctly identify the qualified program committee members and make informed decisions about your manuscript.
  6. How do paper awards work in the new area model?
    Best paper awards and honorable mentions are given out for a certain percentage of all papers. Area paper chairs will nominate papers from their area for these awards. Final decisions on awards will be made independent of areas by a separate committee.
  7. What happens if my co-authors or I are in conflict with both area paper chairs?
    In case of a conflict of interest with both area paper chairs, you need to send your paper to another area. If you are only in conflict with one of them, that can be handled, and it’s fine to submit. To judge whether you are in a conflict of interest refer to the VGTC ethics guidelines for reviewers - which equally apply here. You may also check with the area paper chairs to verify your conflict of interest in case of doubt.
  8. How will areas be reflected in the IEEE VIS program?
    Areas are only an administrative division in the reviewing process. Areas will not be reflected anywhere in the conference program. For example, sessions will be curated independent of the areas papers were submitted to.
  9. How do VIS areas differ from areas/subcommittees in other conferences?
    In contrast to ACM CHI’s subcommittees, IEEE VIS has no area-specific program committee. Also, at VIS, acceptance is recommended by the area paper chairs and confirmed by the overall paper chairs. At CHI, the area-specific program committee makes a joint decision in the program committee meeting.
  10. Some areas don’t seem as coherent as others. Is that true?
    For some areas, the main subject has enjoyed more structured development over the years and has established a set of relatively well-defined topics. In other cases, an area encompasses a few topics that could potentially become separate areas in the future when the research activities on these topics have reached a certain scale. The area model is expected to evolve in response to the emergence of new topics and the rapid development of some existing topics. This change will be managed by a dedicated area curation committee (review the restructuring proposal and future governance documentation for details).
  11. Which areas will have the best acceptance rates?
    The final acceptance decisions are coordinated by the OPCs with an eye towards a balanced program of consistent quality. There may be variations in acceptance rates across areas, and across years. Variation is a natural feature of a dynamic and vibrant process. Authors should decide which area is most suitable for their paper based on intellectual fit and scope; past numbers may not predict future patterns.
  12. Whom should I contact if I have a question about a specific area?
    Please contact the area chairs of that specific area.
  13. Why are APCs banned from submitting to their own area?
    It may appear unusual that APCs cannot submit to their own area (“self-submission”), given that strong expertise in exactly their area is one of the reasons they are appointed. reVISe considered many alternatives, but ultimately proposed this model for the following reasons:
    • Without self-submissions, avoiding conflicts of interest and leaks of information about the review process at the chair level becomes a realistic goal. As APCs do not have access to information about other areas’ papers, they cannot inadvertently learn about the reviews and reviewers of their own papers, and are completely removed from the decision process happening in other areas that involve their own papers. Avoiding conflicts of interest and potential sources for deanonymization increase the trust that authors have in the review process overall.
    • The unified program committee allows the selection of expert reviewers, supported by the new keyword mechanisms, independently of the area a paper is submitted in. Furthermore, in the analysis of the area model during its development, it became apparent that many papers fit well in several areas. Hence, for the vast majority of cases, not allowing self-submission will not compromise the quality of reviewing, and avoids more drastic restrictions, such as disallowing APC submissions completely.
    • In some cases, an APC paper will not fit well into another area. However, experience with journal editors shows that a managing editor does not have to be an expert in an area, as long as they can rely on a pool of qualified reviewers, which is ensured by the unified PC. The choice to block APCs from submitting was balanced against other considered solutions such as involving additional shadow paper chairs or chairs from other areas to help out in cases of conflict. The current solution is the one that involves the least chances to reveal anonymous information and the one that is administratively the most simple solution. Hence, the approach taken balances potential unpleasantness with process simplicity and transparency.
    • Finally, the choice to block APCs from submitting to their own area is consistent with practices in other conferences. Paper chair positions are considered an honor taken on by senior community members. Many who fill these roles are in a position in their scientific careers at which they can give priority to service to the community, yet they still are actively involved in scientific research and publication. It is not unusual in other communities to entirely disallow submissions of chairs overseeing a papers process (for example, all SIGPLAN conferences, including POPL, and PLDI; theory conferences such as STOC, FOCS, SODA, SOCG, ICALP, ESA). The model at VIS offers a compromise between striving for reviewing quality and integrity and allowing APCs to still contribute to the scientific content of the conference.

As all other aspects of the area model, this will be closely watched by ACC, and alternatives will be considered if the need arises.

Version History of the Area Model