(Please accept our apologies for multiple postings of this announcement)

---> Deadline Extended: March 15, 2021 <---

#### CALL FOR PAPERS ***************************

Special issue of the Big Data Research Journal (Elsevier) on Visualization in Big Data and Data Science

#### Guest editors *****************************

- Giuseppe Polese, University of Salerno
- Vincenzo Deufemia, University of Salerno
- Kawa Nazemi, Darmstadt University of Applied Sciences

#### Important dates and timeline **************

- Initial submission: March 15, 2021 (Extended)
- Author Notification: June 15, 2021
- Revised Manuscript Due: August 1, 2021
- Notification of Acceptance: November 1, 2021
- Final Manuscript Due: November 15, 2021
- Tentative Publication Date: January, 2021

#### Context ***********************************

As witnessed by Covid19 outbreak, big data and data science are becoming vital disciplines in several application domains, mainly due to the great 
availability of big data collections from which it is possible to mine precious patterns of knowledge. The Internet of Things (IoT) industrial revolution 
has furtherly contributed to boost this trend, yielding an increased interest for data analytics techniques capable of working on streams of data and time 
series. This has led to the development of powerful predictive models, including deep neural networks, also thanks to the availability of powerful hardware 
and distributed computing paradigms. Nevertheless, one of the aspects that is hindering the contamination of Big Data and Data Science in many sectors is 
to be ascribed to the difficulty in explaining the rationale underlying complex data analytics processes to stakeholders. 

While it is well-known that the data pre-processing and analytics phases account for about 70% and 20% of the whole data analytics process, respectively, 
there is a remaining 10% of the effort that should be devoted to the visualization and explanation of the results, also known as data journalism. Although 
this is a small percentage, and for this reason overlooked by many data scientists, it is one of the most critical ones in order to draw the attention of 
stakeholders and motivate them to trust and invest on the adoption of Big Data and Data Science technologies. This is true also in artificial intelligence 
where explainable AI is also becoming crucial. 

One way to tackle this problem is to rely on efficient visualization metaphors and intelligent visual interaction paradigms. In this respect, we expect 
inter-disciplinary contributions from several research communities, such as, data mining, big data, machine learning, and human-computer interaction, to 
provide new scalable ways for visualizing the results of complex data analytics processes, including efficient interaction techniques to explore them. 
In particular, they should stimulate the active involvement of stakeholders in the data analytics process by enhancing their understanding capabilities.

#### Topics *************************************

Specific topics within the scope of the call include, but are not limited to, the following:

- User interfaces for data science
- Visualization of time-dependent data, geo-localized data, and maps
- Data to visualization mappings
- Interactive knowledge discovery
- Visual operators on data and knowledge
- Big data visualization in various domains (health, education, politics, …)
- Visual exploration of datasets
- Visualization of data correlations
- Visual data mining and visual knowledge discovery
- Visualization of learning models
- Visualization techniques for data profiles
- Visualization for IoT data analytics
- Visual analytics for explainable AI
- Collaborative visual analytics and data science
- Adaptive visual analytics
- Visual trend analytics
- User evaluations and case studies, reports on real cases and experiments

##### Format *************************************

All submitted papers must be clearly written in English and must contain only original work, which has not been published by, or is currently under review 
for, any other journal, conference, symposium, or workshop. Submissions are expected to not exceed 30 pages (including figures, tables, and references) in 
the journal's single-column format using 11-point font. Detailed submission guidelines are available under "Guide for Authors" at:

All manuscripts and any supplementary material should be submitted through the Elsevier Editorial System (EES). The authors must select 
"VSI: Big Data Science Vis" as Article Type when they reach the Article Type step in the submission process. 
The EES website is located at: http://ees.elsevier.com/bdr

All papers will be peer-reviewed by at least three independent reviewers. Requests for additional information should be addressed to the guest editors.

##### Contacts ***********************************

For any question, do not hesitate to contact:

Giuseppe Polese
Managing Guest Editor of the Special Issue

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