BigVis 2020: 3rd International Workshop on Big Data Visual Exploration and Analytics
https://bigvis.imsi.athenarc.gr/bigvis2020
In conjunction with EDBT/ICDT 2020, March 30, 2020, Copenhagen, Denmark

Held in conjunction with the 23rd Intl. Conference on Extending Database Technology & 23rd Intl. Conference on Database Theory (EDBT/ICDT 2020)

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 overplotting. 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
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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, high-dimensional, graph
- Human-in-the-loop processing
- Human-centered databases
- Data modeling, storage, indexing, caching, prefetching & query processing for interactive applications
- Interactive 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


Submissions
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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]


Important Dates
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Submission: January 5, 2020
Notification: January 24, 2020
Camera-ready: January 29, 2020
Workshop: March 30, 2020


Organizing Committee
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David Auber, University Bordeaux, France
Nikos Bikakis, University of Ioannina, Greece
Panos Chrysanthis, University of Pittsburgh, USA
George Papastefanatos, ATHENA Research Center, Greece
Mohamed Sharaf, United Arab Emirates University, UAE


Special Issue
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Extended versions of the best papers of BigVis 2020 will be invited for submission in a special issue of an international journal.


Program Committee
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James Abello, Rutgers Univ, USA
Gennady Andrienko, Fraunhofer, Germany
Rick Cole, Tableau
Alfredo Cuzzocrea, ICAR-CNR & Univ of Calabria, Italy
Danyel Fisher, Honeycomb.io
Steffen Frey, Univ of Stuttgart, Germany
Giorgos Giannopoulos, ATHENA Research Center, Greece
Parke Godfrey, Univ of York, Canada
Michael Gubanov, Univ of Texas at San Antonio, USA
Silu Huang, Microsoft
Christophe Hurter, Ecole Nationale de l’Aviation Civile, France
Eser Kandogan, IBM
James Klosowski, AT&T Research
Zhicheng Liu, Adobe
Steffen Lohmann, Fraunhofer, Germany
Ioana Manolescu, INRIA & Ecole Polytechnique, France
Marios Meimaris, ATHENA Research Center, Greece
Silvia Miksch, Vienna University of Technology, Austria
Davide Mottin, Hasso Plattner Institute, Germany
Martin Nöllenburg, Vienna Univ of Technology, Austria
Olga Papaemmanouil, Brandeis Univ, USA
Paul Parsons, Purdue Univ, USA
Laura Po, Unimore, Italy
Giuseppe Polese, University of Salerno, Italy
Alexander Rind, St. Pölten Univ of Applied Sciences, Austria
Gerik Scheuermann, Univ of Leipzig, Germany
Heidrun Schumann, Univ of Rostock, Germany
Michael Sedlmair, Univ of Stuttgart, Germany
Thibault Sellam, Columbia Univ, USA
Bettina Speckmann, Eindhoven Univ of Technology, Netherlands
Kostas Stefanidis, Univ of Tampere, Finland
Cagatay Turkay, Univ of Warwick, UK
Panos Vassiliadis, Univ of Ioannina, Greece
Junpeng Wang, Visa Research
Chaoli Wang, Univ of Notre Dame, USA
Panpan Xu, Bosch Research
Kai Xu, Middlesex Univ, UK
Hongfeng Yu, Univ of Nebraska-Lincoln, USA