Call for Papers
Empirical Research
Empirical research aims to contribute research methodologies or concrete results of assessments of a visualization / visual analytics contribution or its context of use. Topic of interest include:
Research Methodology: general methodologies for conducting VIS 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 VIS 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.) and cognitive factors (e.g., memory, learning, reasoning, decision-making, problem-solving, knowledge, emotion, etc.)
Topics of interest include:
Application Domains: The use of visualization and visual analytics spreads across essentially all areas research and is relevant to commercial entities as well as non-profit and governmental agencies. In some areas the use has reached a high level of maturity whereas in other domains visualization is emerging as a new and essential component in the workflow. VIS welcomes submissions related to application domains spanning all existing, emerging and potential domains.
Application-specific Technical Solutions: visual representations, interaction techniques, algorithms, techniques, hardware prototypes, software prototypes, integrated workflows, recommended working practice, etc.
Insight Documentation: success stories and failures about applying visualization technology in practice, achievements of multidisciplinary research projects, benefits gained from collaboration with domain experts, and guidelines resulting from application-focused design studies.
Topics of interest include:
Computing Platforms: commodity hardware, GPU, HPC, energy efficient visualization algorithms and hardware, etc.
Visualization Environments: non-immersive and immersive environments, desktop, mobile, web-based, VR/MR/AR, dome theaters, CAVEs, physicalization, remote collaboration, 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, 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, word/tag cloud, etc.
Other Synthesis Techniques: fabrication, sonification, haptic feedback, etc.
Topics of interests include:
Visual 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 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, magic lens, query-based exploration, direct manipulation, interactive deformation,natural interaction, user-adaptive interaction, interoperation between interaction and visualization tasks, editing tools, collaborative visualization, etc.
Visual Communication Techniques: focus+context design, 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 visual interaction, learning UI models for automated capabilities in visualization systems.
Technical Discourses on Visual Representations and Interaction Techniques: visual and interactional metaphors, scalability of visual mapping, and interaction costs, 2D vs. 3D representations, static vs. animated representations, visualization literacy, etc.
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.
Topics of interest include:
Integrated Workflows for Information Seeking, Knowledge Discovery, and Decision Making: Typical technical problems may 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; uncertainty and risk analysis; and so on.
Integrated Workflows for Machine Learning: Typical technical problems may include cleaning and labelling training data; assisting active learning or other semi-automated learning methods; facilitating model testing, evaluation and model comparison; supporting the analysis of learned models and learning processes; enabling model understanding, explanation, refinement, and steering; and monitoring the deployment of machine-learned models as well as other machine-centric processes.
Workflow Optimization: techniques, design patterns, and best practices for designing, developing, evaluating, and improving integrated data intelligence workflows. Methods for analysing and alleviating data biases, machine biases, and human biases.
Knowledge-assisted Workflows: knowledge acquisition, mixed-initiative workflows, real-time guidance and recommendation, provenance management and utilization, post-action review, knowledge sharing, and analyst training in visual data analysis.
