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** Paper submission deadline has been extended to February 4, 2022
(AoE) **<br>
<br>
<br>
BigVis 2022: 5th International Workshop on Big Data Visual
Exploration and Analytics, EDBT/ICDT 2022 <br>
<a class="moz-txt-link-freetext" href="https://bigvis.imsi.athenarc.gr/bigvis2022">https://bigvis.imsi.athenarc.gr/bigvis2022</a><br>
March 29 2022, Edinburgh, UK [Online]<br>
<br>
<br>
Held in conjunction with the 25th Intl. Conference on Extending
Database Technology & 25th Intl. Conference on Database Theory
(EDBT/ICDT 2022)<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>
_Human-AI Collaboration_<br>
BigVis 2022 will devote a session to Human-AI collaboration
approaches in the context of Big data visualization and analytics.<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>
<br>
Important Dates<br>
------------------------------------<br>
Submission: February 4 (AoE) ***Extended***<br>
Notification: February 20, 2022<br>
Camera-ready: February 25, 2022<br>
Workshop: March 29, 2022 [Online]<br>
<br>
<br>
Special Issue<br>
------------------------------------<br>
TBD<br>
<br>
<br>
Organizing Committee<br>
------------------------------------<br>
Nikos Bikakis, ATHENA Research Center, Greece<br>
Hanna Hauptmann, Utrecht University, Netherlands<br>
George Papastefanatos, ATHENA Research Center, Greece<br>
Michael Sedlmair, University of Stuttgart, Germany<br>
<br>
<br>
Program Committee<br>
------------------------------------<br>
<br>
James Abello, Rutgers University<br>
Demosthenes Akoumianakis, Hellenic Mediterranean University<br>
Gennady Andrienko, Fraunhofer<br>
Natalia Andrienko, Fraunhofer<br>
Jacob Biehl, University of Pittsburgh<br>
Wei Chen, Zhejiang University<br>
Panos Chrysanthis, University of Pittsburgh<br>
Alfredo Cuzzocrea, University of Calabria<br>
Evanthia Dimara, Université Paris-Sud<br>
Harish Doraiswamy, Microsoft<br>
Mennatallah El-Assady, University of Konstanz<br>
Ahmed Eldawy, University of California, Riverside<br>
Steffen Frey, University of Groningen<br>
Issei Fujishiro, Keio University<br>
Christoph Garth, Technische Universität Kaiserslautern<br>
Parke Godfrey, York University<br>
Hamed Haddadi, Imperial College London<br>
Eser Kandogan, Megagon Labs<br>
Alireza Karduni, Northwestern University<br>
James Klosowski, AT&T Labs Research<br>
Manolis Koubarakis, National University of Athens<br>
Kwan-Liu Ma, University of California, Davis<br>
Stavros Maroulis, National Technical University of Athens<br>
Suvodeep Mazumdar, The University of Sheffield<br>
Silvia Miksch, Vienna University of Technology<br>
Davide Mottin, Aarhus University<br>
Laura Po, Universitá di Modena e Reggio Emilia<br>
Giuseppe Polese, University of Salerno<br>
Sajjadur Rahman, Megagon Labs<br>
Alexander Rind, St. Pölten University of Applied Sciences<br>
Panagiotis Ritsos, Bangor University<br>
Maria Riveiro, Jönköping University<br>
Hans-Jörg Schulz, Aarhus University<br>
Arjun Srinivasan, Tableau<br>
Manuel Stein, Universität Konstanz<br>
Christian Tominski, University of Rostock<br>
Natkamon Tovanich, IRT SystemX<br>
Yannis Tzitzikas, University of Crete & FORTH-ICS<br>
Katerina Vrotsou, Linköping University<br>
Junpeng Wang, Visa Research<br>
Yunhai Wang, Graduate University of Chinese Academy of Sciences<br>
Jules Wulms, Vienna University of Technology<br>
Jiazhi Xia, Central South University<br>
Kai Xu, Middlesex University<br>
Hongfeng Yu, University of Nebraska-Lincoln<br>
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--
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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