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Nikos Bikakis <[log in to unmask]>
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Thu, 7 Jan 2021 17:31:28 +0200
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**Special Issue**
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].


**Special Theme**
Machine Learning and Visualization: BigVis 2021 will devote a session to 
machine learning approaches in the context of Big data visualization and 
analytics.


**Deadline Extension**
Due to numerous requests the submission deadline has been extended to 
**January 18, 2021**


-------------------------------------------------------------

Call for Papers

BigVis 2021: 4th  International Workshop on Big Data Visual Exploration 
and Analytics
https://bigvis.imsi.athenarc.gr/bigvis2021
   March 23, 2021, Nicosia, Cyprus


Held in conjunction with the 24th Intl. Conference on Extending Database 
Technology & 24th Intl. Conference on Database Theory (EDBT/ICDT 2021)

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.

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.

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.


Workshop Topics
------------------------------------
In the context of visual exploration and analytics, topics of interest 
include, but are not limited to:
  - Visualization, exploration & analytics techniques for various data 
types; e.g., stream, spatial, graph
  - Human -in -the -loop processing
  - Human -centered databases
  - Data modeling, storage, indexing, caching, prefetching & query 
processing for interactive applications
  - Interactive & human -centered machine learning
  - Interactive data mining
  - User -oriented visualization; e.g., recommendation, assistance, 
personalization
  - Visualization & knowledge; e.g., storytelling
  - Progressive analytics
  - In -situ visual exploration & analytics
  - Novel interface & interaction paradigms
  - Visual representation techniques; e.g., aggregation, sampling, multi 
-level, filtering
  - Scalable visual operations; e.g., zooming, panning, linking, brushing
  - Scientific visualization; e.g., volume visualization
  - Analytics in the fields of scholarly data, digital libraries, 
multimedia, scientific data, social data, etc.
  - Immersive visualization
  - Interactive computer graphics
  - Setting -oriented visualization; e.g., display resolution/size, 
smart phones, visualization over networks
  - High performance, distributed & parallel techniques
  - Visualization hardware & acceleration techniques
  - Linked Data & ontologies visualization
  - Benchmarks for data visualization & analytics
  - Case & user studies
  - Systems & tools


Special Theme
------------------------------------
   ***Machine Learning and Visualization***
   BigVis 2021 will devote a session to machine learning approaches in 
the context of Big data visualization and analytics.



Submissions
------------------------------------
   Regular/Short Research papers [up to 8/4 pages]
   Work-in-progress papers [up to 4 pages]
   Vision papers [up to 4 pages]
   System papers and Demos [up to 4 pages]

   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.



Special Issue
------------------------------------
   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].



Important Dates
------------------------------------
   Submission: January 18, 2021  ***extended***
   Notification: January 29, 2021
   Camera-ready: February 8, 2021
   Workshop: March 23, 2021


Organizing Committee
------------------------------------
   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


Program Committee
------------------------------------
   James Abello, Rutgers University, USA
   Gennady Andrienko, Fraunhofer, Germany
   Natalia Andrienko, Fraunhofer, Germany
   Michael Behrisch, Utrecht University, Netherlands
   Jacob Biehl, University of Pittsburgh, USA
   Rick Cole, Tableau
   Alfredo Cuzzocrea, University of Calabria, Italy
   Ahmed Eldawy, University of California, Riverside, USA
   Jean-Daniel Fekete, INRIA, France
   Steffen Frey, University of Stuttgart, Germany
   Issei Fujishiro, Keio University, Japan
   Giorgos Giannopoulos, ATHENA Research Center, Greece
   Parke Godfrey, University of York, Canada
   Silu Huang, Microsoft
   Christophe Hurter, Ecole Nationale de l’Aviation Civile, France
   Halldor Janetzko, Lucerne University of Applied Sciences & Arts, 
Switzerland
   Stefan Jänicke, University of Southern Denmark, Denmark
   Vana Kalogeraki, Athens University of Economics & Business, Greece
   Eser Kandogan, IBM
   Anastasios Kementsietsidis, Google
   James Klosowski, AT&T Research
   Stavros Maroulis, National Technical University of Athens, Greece
   Suvodeep Mazumdar, The University of Sheffield, United Kingdom
   Silvia Miksch, Vienna University of Technology, Austria
   Davide Mottin, Aarhus University, Denmark
   Martin Nöllenburg, Vienna University of Technology, Austria
   Behrooz Omidvar-Tehrani, NAVER LABS Europe, France
   Jaakko Peltonen, Aalto University & University of Tampere, Finland
   Laura Po, Unimore, Italy
   Giuseppe Polese, University of Salerno, Italy
   Alexander Rind, St. Pölten University of Applied Sciences, Austria
   Rahman Sajjadur, Megagon Labs
   Hans-Jörg Schulz, Aarhus University, Denmark
   Bettina Speckmann, Eindhoven University of Technology, Netherlands
   Kostas Stefanidis, University of Tampere, Finland
   Christian Tominski, University of Rostock, Germany
   Yannis Tzitzikas, University of Crete & FORTH-ICS, Greece
   Katerina Vrotsou, Linköping University, Sweden
   Chaoli Wang, University of Notre Dame, USA
   Junpeng Wang, Visa Research
   Chen Wei, Zhejiang University, China
   Yingcai Wu, Zhejiang University, China
   Jiazhi Xia, Central South University, China
   Panpan Xu, Bosch Research
   Hongfeng Yu, University of Nebraska-Lincoln, USA


-- 
nikos bikakis
ATHENA Research Center
Greece
www.nbikakis.com

-- 
_nikos bikakis


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