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Call for Papers<br>
<br>
Special Issue "Machine Learning Approaches in Big Data
Visualization"<br>
IEEE Computer Graphics and Applications (CG&A)<br>
<a class="moz-txt-link-freetext" href="https://bit.ly/37EaNcn">https://bit.ly/37EaNcn</a><br>
<br>
<br>
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.<br>
<br>
This special issue aims to publish novel works on
multidisciplinary research areas spanning from data management and
ML to visualization and human-computer interaction. <br>
<br>
<br>
Topics for the Special Issue<br>
------------------------------- <br>
Topics of interest include, but are not limited to:<br>
<br>
- Visualization, exploration, and analytics techniques for various
data types (for example: text, stream, field, high-dimensional,
graph, and temporal)<br>
- ML-driven visualization<br>
- Interactive data mining visualization<br>
- Progressive visual analytics<br>
- Data modeling, storage, indexing, caching, prefetching, and
query processing for interactive applications<br>
- User-oriented visualization (for example: recommendation,
assistance, and personalization)<br>
- Visual representation techniques (for example: aggregation,
sampling, multi-level, and filtering)<br>
- In-situ visual exploration and analytics<br>
- Immersive visualization<br>
- Setting-oriented visualization (for example: display size, smart
phones, and visualization over networks)<br>
- High-performance, distributed, and parallel techniques<br>
- Visualization hardware and acceleration techniques for
visualization<br>
- Benchmarks for data visualization and analytics<br>
<br>
<br>
Deadlines<br>
------------------------------- <br>
Submissions due: 29 October 2021<br>
Publication: May/June 2022<br>
<br>
<br>
Submission Guidelines<br>
------------------------------- <br>
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.<br>
<br>
<br>
Guest Editors<br>
------------------------------- <br>
Nikos Bikakis, ATHENA Research Center, Greece<br>
Panos K. Chrysanthis, University of Pittsburgh, USA<br>
George Papastefanatos, ATHENA Research Center, Greece<br>
Tobias Schreck, Graz University of Technology, Austria<br>
<br>
Contact the guest editors at <a class="moz-txt-link-abbreviated" href="mailto:cga3-2022@computer.org">cga3-2022@computer.org</a><br>
<br>
<br>
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<pre class="moz-signature" cols="200">--
_ nikos</pre>
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