View source for JOGC Special Issue 2017 : Journal of Grid Computing Special Issue on Big Data and Digital Transformation

{{Event 
|Acronym=JOGC Special Issue 2017 
|Title=JOGC Special Issue  2017 : Journal of Grid Computing Special Issue on Big Data and Digital Transformation 
|Type=Conference 
|Field=big data
|Start date=Invalid date
|End date=2017/08/21T09:54:04 
|City=N/A 
|Country=undefined
|Homepage=http://www.springer.com/computer/communication+networks/journal/10723/PSE
|Submission deadline=2017/08/31T12:00:00
}}
Journal of Grid Computing
(2016 Impact Factor 2.766; Q2)
Special Issue on “Big Data and Digital Transformation”

Introduction
The reality we live in is more and more of a digital nature. This Digital
Transformation embraces all of society and is the cause of disruptive changes.
Indeed, from smart cities and urban computing to virtual enterprises, social
media and Internet of Things (IoT) solutions, our lives are progressively being
digitalized.
In the era of Digital Transformation, the distributed nature and scalability
requirements of the new ICT solutions are the norm. These, combined with the
large volumes of data which are being generated at increasingly higher paces
coming from a multitude of sources, require the need for innovative data
(analysis) solutions. So, Digital Transformation and Big Data solutions are
deeply related. One cannot happen without the other.
This Big Data and Digital Transformation special issue aims to publish research
work spanning across fields as diverse as computer science and engineering,
information systems and software, business and social sciences, describing
innovative approaches to (distributed) Big Data solutions and applications in
the realm of Digital Transformation.

Special Issue Scope
Topics of particular interest include, but are not limited to:
• Big Data infrastructures for Virtual Enterprises;
• Internet of Things (IoT) and Big Data Solutions;
• Industry 4.0 and Big Data Solutions;
• Big Data solutions for Smart Cities;
• Urban Computing and Big Data;
• Big Data technologies and applications;
• Big Data platforms and storage solutions;
• Big Data and Cloud Computing;
• Big Data Architectures;
• Big Data and Analytics;
• Cloud Platforms for Big Data Analysis;
• Data Quality approaches to Big Data;
• Cloud / Networks infrastructure for Big Data;
• Case Studies in Digital Transformation using Big Data solutions;
• Innovative Applications of Big Data solutions.
In addition to the above list of topics, we welcome submissions on other topics
addressing the theme of Big Data solutions to Digital Transformation needs.

Important Dates
Paper submission due date: Aug 31th, 2017
Notification of acceptance: Oct 31th, 2017
Revised version due date: Nov 30th, 2017
Notification of final acceptance: Dec 15th, 2017
Submission of final revised paper: Dec 31th, 2017
Expected publication date: Early 2018

Submission
Besides significantly extended versions of selected papers from the 5th World
Conference on Information Systems and Technologies (WorldCist’17), this special
issue invites original research papers that present novel ideas in the field.
Every submitted paper will be peer reviewed according to the usual standards of
the journal, and will be evaluated on the basis of originality, quality and
relevance to this Special Issue. Papers should be submitted directly to the
Journal of Grid Computing at https://www.editorialmanager.com/grid/. Detailed
submission instructions for authors are available at the website. When
submitting authors must indicate that their manuscript is intended for the "Big
Data and Digital Transformation" Special Issue. Requests for additional
information should be addressed to the guest editors.

Editor-in-Chief
● Peter Kacsuk, MTA SZTAKI Computer and Automation Research Institute, Budapest,
Hungary and the University of Westminster, London, UK

Guest Editors (by alphabetical order)
● José Luís Pereira, University of Minho, Portugal (jlmp@dsi.uminho.pt)
● Orlando Belo, University of Minho, Portugal (obelo@di.uminho.pt)
● Pascal Ravesteijn, University of Applied Sciences Utrecht, The Netherlands
(pascal.ravesteijn@hu.nl)