We are co-organizing the International Symposium Visual Analytics and Data Science at the next International Information Visualisation Conference (iV 2021), which is held online. The Information Visualisation Conference (iV) is an international conference that aims to provide a foundation for integrating the human-centered, technological and strategic aspects of information visualization to promote international exchange, cooperation and development. The proceedings will be published as usual in IEEE Xplore.
Visual Analytics is viewed as the science of analytical reasoning empowered by interactive visualizations. It combines interactive visualizations with models and approaches of machine learning and artificial intelligence, enabling solving complex analytical tasks by uncovering hidden patterns in data.
The research on Visual Analytics is closely related to that of Data Science. Both areas seek to enhance the knowledge discovery process using machine learning, data mining, and artificial intelligence methods, whereas Visual Analytics allows commonly a direct manipulation of the underlying models through graphical representations. By leveraging human perception of the visual space, patterns that might not otherwise be discovered. Visual Analytics utilizes concepts from a wide variety of disciplines, including Computer Graphics, Information Visualization, Machine Learning, Artificial Intelligence, Knowledge Discovery, Cognition, and Visual Perception.
Papers on all aspects of Visual Analytics and Data Science are solicited. Papers will be refereed and appear in the main conference proceedings published by Conference Publishing Services CPS – Conference Publishing Services, – Library of Congress/ISSN, ISBN, and other bibliographical registration details; Arrange for indexing through INSPEC, EI (Compendex), Thomson ISI, and other indexing services.
A selection of the best papers will be recommended for publication in special issues of scientific journals, or as an edited book.
Topics of interest:
- Combining visual and computational methods of data analysis
- Visual querying
- Visual analytics of spatial, temporal, and spatiotemporal data
- Knowledge construction and management in visual analytics
- Privacy issues in visual analytics
- Cognitive approaches and explanations for visual data mining
- Visualization of the data mining algorithm
- Scalability issues
- Empirical studies of performance
- Evaluation of visual data mining methods
- Collaborative visualization and mining
- Applications of visual data mining and analytics
- Case studies
- Computational steering for long-running data mining applications
- Reviews and surveys of related literature
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