**Deadline Extension**
Due to requests the submission deadline has been extended to **January 24, 2021**

**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.


Call for Papers

BigVis 2021: 4th  International Workshop on Big Data Visual Exploration and Analytics
  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.

  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 24, 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