[Infovis] [Special Issue CFP] Interactive Big Data Visualization and Analytics, Big Data Research Journal, Elsevier

[Nikos Bikakis]

Call for Papers

Special Issue "Interactive Big Data Visualization and Analytics"
Big Data Research Journal, Elsevier (Impact Factor: 2.95)
https://www.journals.elsevier.com/big-data-research/call-for-papers/big-data-visualization-and-analytics


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.

This special issue aims to publish work on multidisciplinary research areas spanning from Data Management and Mining to Information Visualization and Human-Computer Interaction.


Topics for the Special Issue
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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



Important Dates
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   Submission Deadline: October 1, 2020
   Author Notification: December 1, 2020
   Revised Manuscript Due: January 15, 2021
   Notification of Acceptance: February 1, 2021
   Final Manuscript Due: March 1, 2020
   Tentative Publication Date: May, 2021



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