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**Special Issue** <br>
Extended versions of the best papers will be invited for
submission to a Special Issue of the IEEE Computer Graphics
and Applications (CG&A) [pending final decision]. <br>
<br>
<br>
**Special Theme**<br>
Machine Learning and Visualization: BigVis 2021 will devote
a session to machine learning approaches in the context of
Big data visualization and analytics. <br>
<br>
<br>
**Deadline Extension**<br>
Due to numerous requests the submission deadline has been
extended to **January 18, 2021** <br>
<br>
<br>
------------------------------------------------------------- <br>
<br>
Call for Papers <br>
<br>
BigVis 2021: 4th International Workshop on Big Data Visual
Exploration and Analytics <br>
<a class="moz-txt-link-freetext"
href="https://bigvis.imsi.athenarc.gr/bigvis2021">https://bigvis.imsi.athenarc.gr/bigvis2021</a>
<br>
March 23, 2021, Nicosia, Cyprus <br>
<br>
<br>
Held in conjunction with the 24th Intl. Conference on
Extending Database Technology & 24th Intl. Conference on
Database Theory (EDBT/ICDT 2021) <br>
<br>
Information Visualization is nowadays one of the
cornerstones of Data Science, turning the abundance of Big
Data being produced through modern systems into actionable
knowledge. Indeed, the Big Data era has realized the
availability of voluminous datasets that are dynamic, noisy
and heterogeneous in nature. Transforming a data-curious
user into someone who can access and analyze that data is
even more burdensome now for a great number of users with
little or no support and expertise on the data processing
part. Thus, the area of data visualization, visual
exploration and analysis has gained great attention
recently, calling for joint action from different research
areas from the HCI, Computer graphics and Data management
and mining communities. <br>
<br>
In this respect, several traditional problems from these
communities such as efficient data storage, querying &
indexing for enabling visual analytics, new ways for visual
presentation of massive data, efficient interaction and
personalization techniques that can fit to different user
needs are revisited. The modern exploration and
visualization systems should nowadays offer scalable
techniques to efficiently handle billion objects datasets,
limiting the visual response in a few milliseconds along
with mechanisms for information abstraction, sampling and
summarization for addressing problems related to visual
information over-plotting. Further, they must encourage user
comprehension offering customization capabilities to
different user-defined exploration scenarios and preferences
according to the analysis needs. Overall, the challenge is
to offer self-service visual analytics, i.e. enable data
scientists and business analysts to visually gain value and
insights out of the data as rapidly as possible, minimizing
the role of IT-expert in the loop. <br>
<br>
The BigVis workshop aims at addressing the above challenges
and issues by providing a forum for researchers and
practitioners to discuss, exchange, and disseminate their
work. BigVis attempts to attract attention from the research
areas of Data Management & Mining, Information
Visualization and Human-Computer Interaction and highlight
novel works that bridge together these communities. <br>
<br>
<br>
Workshop Topics <br>
------------------------------------ <br>
In the context of visual exploration and analytics, topics
of interest include, but are not limited to: <br>
- Visualization, exploration & analytics techniques for
various data types; e.g., stream, spatial, graph <br>
- Human -in -the -loop processing <br>
- Human -centered databases <br>
- Data modeling, storage, indexing, caching, prefetching
& query processing for interactive applications <br>
- Interactive & human -centered machine learning <br>
- Interactive data mining <br>
- User -oriented visualization; e.g., recommendation,
assistance, personalization <br>
- Visualization & knowledge; e.g., storytelling <br>
- Progressive analytics <br>
- In -situ visual exploration & analytics <br>
- Novel interface & interaction paradigms <br>
- Visual representation techniques; e.g., aggregation,
sampling, multi -level, filtering <br>
- Scalable visual operations; e.g., zooming, panning,
linking, brushing <br>
- Scientific visualization; e.g., volume visualization <br>
- Analytics in the fields of scholarly data, digital
libraries, multimedia, scientific data, social data, etc. <br>
- Immersive visualization <br>
- Interactive computer graphics <br>
- Setting -oriented visualization; e.g., display
resolution/size, smart phones, visualization over networks <br>
- High performance, distributed & parallel techniques <br>
- Visualization hardware & acceleration techniques <br>
- Linked Data & ontologies visualization <br>
- Benchmarks for data visualization & analytics <br>
- Case & user studies <br>
- Systems & tools <br>
<br>
<br>
Special Theme <br>
------------------------------------ <br>
***Machine Learning and Visualization*** <br>
BigVis 2021 will devote a session to machine learning
approaches in the context of Big data visualization and
analytics. <br>
<br>
<br>
<br>
Submissions <br>
------------------------------------ <br>
Regular/Short Research papers [up to 8/4 pages] <br>
Work-in-progress papers [up to 4 pages] <br>
Vision papers [up to 4 pages] <br>
System papers and Demos [up to 4 pages] <br>
<br>
For the first time, BigVis will give a Best Paper Award.
Best paper will be accompanied with a monetary prize,
sponsored by the Visual Facts project. <br>
<br>
<br>
<br>
Special Issue <br>
------------------------------------ <br>
Extended versions of the best papers will be invited for
submission to a Special Issue of the IEEE Computer Graphics
and Applications (CG&A) [pending final decision]. <br>
<br>
<br>
<br>
Important Dates <br>
------------------------------------ <br>
Submission: January 18, 2021 ***extended*** <br>
Notification: January 29, 2021 <br>
Camera-ready: February 8, 2021 <br>
Workshop: March 23, 2021 <br>
<br>
<br>
Organizing Committee <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>
<br>
Program Committee <br>
------------------------------------ <br>
James Abello, Rutgers University, USA <br>
Gennady Andrienko, Fraunhofer, Germany <br>
Natalia Andrienko, Fraunhofer, Germany <br>
Michael Behrisch, Utrecht University, Netherlands <br>
Jacob Biehl, University of Pittsburgh, USA <br>
Rick Cole, Tableau <br>
Alfredo Cuzzocrea, University of Calabria, Italy <br>
Ahmed Eldawy, University of California, Riverside, USA <br>
Jean-Daniel Fekete, INRIA, France <br>
Steffen Frey, University of Stuttgart, Germany <br>
Issei Fujishiro, Keio University, Japan <br>
Giorgos Giannopoulos, ATHENA Research Center, Greece <br>
Parke Godfrey, University of York, Canada <br>
Silu Huang, Microsoft <br>
Christophe Hurter, Ecole Nationale de l’Aviation Civile,
France <br>
Halldor Janetzko, Lucerne University of Applied Sciences
& Arts, Switzerland <br>
Stefan Jänicke, University of Southern Denmark, Denmark <br>
Vana Kalogeraki, Athens University of Economics &
Business, Greece <br>
Eser Kandogan, IBM <br>
Anastasios Kementsietsidis, Google <br>
James Klosowski, AT&T Research <br>
Stavros Maroulis, National Technical University of Athens,
Greece <br>
Suvodeep Mazumdar, The University of Sheffield, United
Kingdom <br>
Silvia Miksch, Vienna University of Technology, Austria <br>
Davide Mottin, Aarhus University, Denmark <br>
Martin Nöllenburg, Vienna University of Technology,
Austria <br>
Behrooz Omidvar-Tehrani, NAVER LABS Europe, France <br>
Jaakko Peltonen, Aalto University & University of
Tampere, Finland <br>
Laura Po, Unimore, Italy <br>
Giuseppe Polese, University of Salerno, Italy <br>
Alexander Rind, St. Pölten University of Applied Sciences,
Austria <br>
Rahman Sajjadur, Megagon Labs <br>
Hans-Jörg Schulz, Aarhus University, Denmark <br>
Bettina Speckmann, Eindhoven University of Technology,
Netherlands <br>
Kostas Stefanidis, University of Tampere, Finland <br>
Christian Tominski, University of Rostock, Germany <br>
Yannis Tzitzikas, University of Crete & FORTH-ICS,
Greece <br>
Katerina Vrotsou, Linköping University, Sweden <br>
Chaoli Wang, University of Notre Dame, USA <br>
Junpeng Wang, Visa Research <br>
Chen Wei, Zhejiang University, China <br>
Yingcai Wu, Zhejiang University, China <br>
Jiazhi Xia, Central South University, China <br>
Panpan Xu, Bosch Research <br>
Hongfeng Yu, University of Nebraska-Lincoln, USA <br>
<br>
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<pre class="moz-signature" cols="72">--
nikos bikakis
ATHENA Research Center
Greece
<a class="moz-txt-link-abbreviated" href="http://www.nbikakis.com">www.nbikakis.com</a>
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