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face="Calibri"><br>
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>
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<pre class="moz-signature" cols="200">--
Nikos Bikakis
Information Management Systems Institute
ATHENA Research Center
Athens | Greece
<a class="moz-txt-link-abbreviated" href="http://www.nbikakis.com">www.nbikakis.com</a>
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