CLOSED Call for Papers: Special Issue on Machine Learning Approaches in Big Data Visualization

Data visualization is now one of the cornerstones of data science, turning the abundance of big data being produced through modern systems into actionable knowledge. Data visualization in the big data era raises the need to co-design and more closely align the underlying data management systems with the user-oriented techniques that state-of-the-art visualization systems now offer. Several solutions from those two communities are revisited with big data in mind, such as efficient data storage, adaptive indexing for enabling visual interaction and visual analytics, machine learning (ML)-driven visualization and new ways for visual presentation of massive data, and personalization and automation techniques that can fit to different users’ needs. Overall, modern visualization systems start integrating scalable techniques to efficiently support complex ML-based analysis over billion-object datasets, while limiting the visual response to a few milliseconds.

This special issue aims to publish novel works on multidisciplinary research areas spanning from data management and ML to visualization and human-computer interaction. Topics of interest include, but are not limited to:

• Visualization, exploration, and analytics techniques for various data types (for example: text, stream, field, high-dimensional, graph, and temporal)
• ML-driven visualization
• Interactive data mining visualization
• Progressive visual analytics
• Data modeling, storage, indexing, caching, prefetching, and query processing for interactive applications
• User-oriented visualization (for example: recommendation, assistance, and personalization)
• Visual representation techniques (for example: aggregation, sampling, multi-level, and filtering)
• In-situ visual exploration and analytics
• Immersive visualization
• Setting-oriented visualization (for example: display size, smart phones, and visualization over networks)
• High-performance, distributed, and parallel techniques
• Visualization hardware and acceleration techniques for visualization
• Benchmarks for data visualization and analytics

Deadlines

Submissions due: 29 October 2021

Publication: May/June 2022

Submission Guidelines

Please see the author information on how to submit a manuscript. Please submit your papers through the ScholarOne online system and be sure to select this special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.

Guest Editors

Contact the guest editors at cga3-2022@computer.org.

• Nikos Bikakis, ATHENA Research Center, Greece
• Panos K. Chrysanthis, University of Pittsburgh, USA
• George Papastefanatos, ATHENA Research Center, Greece
• Tobias Schreck, Graz University of Technology, Austria