The IEEE Conference on Visual Analytics Science and Technology (VAST) solicits original research papers on a set of diverse topics related to visual analytics. These papers may contribute towards new methods for human-in-the-loop computation; visualization and interaction techniques; representation of data and knowledge; models of analytical reasoning and discourse; and applications and systems of visual analytics to a broad range real-world contexts and domains.
Following the successful two-track arrangement in previous years, IEEE VAST 2020 will again feature an expanded set of accepted papers in two categories:
TVCG Track: Papers that exhibit the highest quality in terms of originality, rigor and significance will appear in a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG). The acceptance rate is anticipated to be similar to that of past years (around 22%-25%), subject to the decisions resulting from the review process. After initial notification of review results, conditionally accepted papers (including supplemental material) will undergo a revision and review cycle in order to ensure that they are acceptable for publication and presentation in the journal. The paper and supplemental material will also be submitted to the IEEE Digital Library, subject to its standard terms and conditions.
Conference-only Track:Top quality and timely, innovative VAST submissions may be accepted for the conference-only track. Those papers, which feature new contributions, will be presented as Conference Papers during IEEE VAST, and will be included on the IEEE VIS USB Proceedings. After initial notification of review results, conditionally accepted papers (including supplemental material) will undergo a revision and approval cycle. The paper and supplemental material will be submitted to the IEEE Digital Library subject to its standard terms and conditions.
Visual analytics is the science of analytical reasoning supported by highly interactive visual interfaces. People use visual analytics tools and techniques in all aspects of science, engineering, business, and government to synthesize information into knowledge; derive insight from massive, dynamic, and often conflicting data; detect the expected and discover the unexpected; provide timely, defensible, and understandable assessments; and integrate analytic processes into decisions and innovative plans of action. The issues stimulating this body of research provide a grand challenge in science: turning information overload into a significant opportunity. Visual analytics requires interdisciplinary science, going beyond traditional scientific and information visualization to include statistics, mathematics, knowledge representation, management and discovery technologies, cognitive and perceptual sciences, decision sciences, and more. Your submission should help develop and/or apply visual analytics, clearly showing an interdisciplinary approach.
Research contributions are welcomed across a range of topics including, but not limited to:
Individual and collaborative reasoning including cognition and perception, analytic discourse, knowledge discovery, creativity and expertise, and operational, ethical, and value-based decision-making using interactive visualization systems.
Integration of data analysis, interaction, and visualization, including the use of machine learning, artificial intelligence, and deep learning techniques to support interactive analysis.
Visual representations and interaction techniques including the principles for depicting information, new visual paradigms, statistical graphics, geospatial visualizations, the science of interaction, and approaches for generating visual analytic visualization and interactions.
Data management and knowledge representation including scalable data representations for high volume and stream data, statistical and semantic signatures, and synthesis of information from diverse data sources.
Presentation, production, and dissemination methods including methods and tools for capturing the analytics process, methods for elicitation of stakeholder constraints, priorities & processes for incorporation in analysis, and storytelling for specific and varying audiences.
Applications of visual analysis techniques, including but not limited to applications in science, engineering, humanities, business, public safety, commerce, and logistics as far as they contribute to visual analytics are of particular interest.
Explainable AI and trust in machine learning and automation, including the design and use of novel visual and interactive techniques that help users to understand, appropriately trust, and effectively manage artificially intelligent machine partners
Evaluation methods, including ethical analysis, privacy, security, & regulatory compliance, interoperability, and application practice & experience.
Devices and technologies which are fundamental for visual analytics, including user and device adaptivity, web interfaces and mobile or other novel devices.
VIS papers often fall into one or many of five main categories: technique, system, design study, evaluation, or model. Although your main paper type has to be specified during the paper submission process, papers can include elements of more than one of these categories. In visual analytics, concepts, theories, algorithms, techniques, designs, systems, empirical studies and applications normally create a context where analysis, visualization and interaction are integrated to optimize the combination of human and machine capabilities. It is this context that differentiates VAST from other conferences in VIS. Data involved can be spatial or non-spatial, and techniques can be human-centric or machine-centric. Furthermore, the application domain can be almost any academic discipline, industry, business sector, or governmental operation.
Paper Type: Technique & Algorithm
A technique paper introduces a novel technique or algorithm that has not previously appeared in the literature, or that significantly extends known techniques or algorithms. The technique or algorithm description provided in the paper should be complete enough that a competent graduate student in visualization could implement the work, and the authors should create a prototype implementation of the methods. This technique should ideally be of general application rather than being restricted to a single task or single source of data, and the exposition should be focused on what the technique does, how it does it, the tasks and datasets for which this new method is appropriate, and what the computational and other costs are. Evaluation is likely to strengthen technique papers.
T. Fujiwara, O. Kwon and K. Ma, “Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning,” in IEEE Transactions on Visualization and Computer Graphics. doi: 10.1109/TVCG.2019.2934251. VAST 2019 Honorable Mention.
D. Liu, P. Xu and L. Ren, “TPFlow: Progressive Partition and Multidimensional Pattern Extraction for Large-Scale Spatio-Temporal Data Analysis,” in IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 1-11, Jan. 2019. doi: 10.1109/TVCG.2018.2865018. VAST 2018 Best Paper.
C. Xie, W. Zhong and K. Mueller, “A Visual Analytics Approach for Categorical Joint Distribution Reconstruction from Marginal Projections,” in IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 51-60, Jan. 2017. doi:10.1109/TVCG.2016.2598479. VAST 2016 Honorable Mention.
Paper Type: System
A system paper describes a solution to a problem where the major task is building a large complex software artifact, applying largely known visual analytics techniques. The system that is described is both novel and important, and has been implemented. Here, the focus should be on the design decisions, the implications for software / hardware structure, and comparison with other systems. The comparison includes specific discussion of how the described system differs from and is, in some significant respects, superior to those systems.
B. Yu and C. T. Silva, “FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System,” in IEEE Transactions on Visualization and Computer Graphics. doi: 10.1109/TVCG.2019.2934668. VAST 2019 Best Paper.
C. Xie, W. Xu and K. Mueller, “A Visual Analytics Framework for the Detection of Anomalous Call Stack Trees in High Performance Computing Applications,” in IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 215-224, Jan. 2019. doi: 10.1109/TVCG.2018.2865026. VAST 2018 Honorable Mention.
P. Xu, H. Mei, L. Ren and W. Chen, “ViDX: Visual Diagnostics of Assembly Line Performance in Smart Factories” in IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 291-300, Jan. 2017. doi: 10.1109/TVCG.2016.2598664. VAST 2016 Honorable Mention.
Paper Type: Application & Design Study
An application or design study paper explores the choices made when applying visualization and visual analytics techniques in an application area to the requirements of the target task. These papers typically include an encapsulated description of a problem domain and the questions to be resolved by visual analytics, then describes the application of visual analytics to the task, any novel techniques developed, and how the visual analytics solution answered the questions posed. The results of the study, including insights generated in the application domain and knowledge generated through the research process, should be clearly conveyed. The work will be judged by the design lessons learned or insights gleaned for visual analytics research – which may or may not include novel techniques, algorithms, or systems – on which future contributors can build. We invite submissions on any application area.
S. Hazarika, H. Li, K. Wang, H. Shen and C. Chou, “NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation,” in IEEE Transactions on Visualization and Computer Graphics. doi: 10.1109/TVCG.2019.2934591. VAST 2019 Honorable Mention.
Dongyu Liu, Di Weng, Yuhong Li, Jie Bao, Yu Zheng, Huamin Qu and Yingcai Wu, “SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations” in IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 1-10, Jan. 2017. doi: 10.1109/TVCG.2016.2598432.
F. Beck, S. Koch and D. Weiskopf, “Visual Analysis and Dissemination of Scientific Literature Collections with SurVis” in IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 180-189, Jan. 31 2016. doi: 10.1109/TVCG.2015.2467757.
Paper Type: Empirical Study
An empirical study paper explores the usage of visual analytics by people, and presents a study, either qualitative or quantitative, of analysis techniques or systems. The research contribution will be judged on the validity and importance of the results, including where appropriate, the definition of hypotheses, tasks, data sets, the rigorous collection and examination/analysis/coding of data, the selection of subjects and cases, as well as validation, discussion and conclusions.
J. Liu, N. Boukhelifa and J. R. Eagan, “Understanding the Role of Alternatives in Data Analysis Practices,” in IEEE Transactions on Visualization and Computer Graphics. doi: 10.1109/TVCG.2019.2934593.
Y. Zhao, F. Luo, M. Chen, Y. Wang, J. Xia, F. Zhou, Y. Wang, Y. Chen and W. Chen, “Evaluating Multi-Dimensional Visualizations for Understanding Fuzzy Clusters,” in IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 12-21, Jan. 2019. doi: 10.1109/TVCG.2018.2865020.
A. Dasgupta, J.-Y. Lee, R. Wilson, R. A. Lafrance, N. Cramer, K. Cook and S. Payne, “Familiarity Vs Trust: A Comparative Study of Domain Scientists’ Trust in Visual Analytics and Conventional Analysis Methods,” in IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 271-280, Jan. 2017. doi: 10.1109/TVCG.2016.2598544.
Paper Type: Theory & Model
A theory or model paper presents new interpretations of the foundational theory of visual analytics. These papers do not require implementation, but contribute by illuminating how techniques complement and exploit properties of human cognition, as well as how researchers conduct effective and rigorous studies.
M. Khayat, M. Karimzadeh, D. S. Ebert and A. Ghafoor, “The Validity, Generalizability and Feasibility of Summative Evaluation Methods in Visual Analytics,” in IEEE Transactions on Visualization and Computer Graphics. doi: 10.1109/TVCG.2019.2934264.
D. Sacha, M. Kraus, D. A. Keim and M. Chen, “VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning,” in IEEE Transactions on Visualization and Computer Graphics. vol. 25, no. 1, pp. 385-395, Jan. 2019. doi: 10.1109/TVCG.2018.2864838.
F. Dabek and J. J. Caban, “A Grammar-based Approach for Modeling User Interactions and Generating Suggestions During the Data Exploration Process,” in IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 41-50, Jan. 2017. doi: 10.1109/TVCG.2016.2598471.
Ross Maciejewski, Arizona State University, USA
Silvia Miksch, Vienna University of Technology (TU Wien), Austria
Jing Yang, University of North Carolina at Charlotte, USA
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