Special Section on Recent Advancements in Big Data Fusion

in Special Issue   Posted on June 21, 2019 

Information for the Special Issue

Submission Deadline: Sat 15 Feb 2020
Journal Impact Factor : 2.663
Journal Name : Computers & Electrical Engineering
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/computers-and-electrical-engineering/call-for-papers/special-section-on-recent-advancements-in-big-data-fusion
Journal & Submission Website: https://www.journals.elsevier.com/computers-and-electrical-engineering

Special Issue Call for Papers:


The term Data Fusion refers to the process of combining data coming from different sources with the goal of producing a more complete, improved and precise information than that provided by each source separatedly. The Data Fusion paradigm has been growing recently due to factors such as sustained increase in systems connectivity, the advent of the Internet of Things (IoT), and the need for dealing with Big Data. In the current distributed environment, where it is possible to find heterogeneous data sources that generate big amounts of data, the use of Data Fusion techniques has demonstrated to be useful to address diffent tasks in various application domains.

Big Data Fusion is strongly linked to current trends such as big data analytics, sensor networks, and the IoT. These are constantly evolving disciplines where new challenges related to data management and explotation arise continuously. In order to deal with these challenges, the Data Fusion paradigm also needs to be continuously updated by means of new methods and architectures that make it possible to mantain its high degree of applicability in different domains.

The aim of this special section is to disseminate the latest advances in Big Data Fusion regarding the new methods, architectures and applications that emerge from the scientific community. It is intended to contain mainly the extended versions of the best papers presented at the International Conference on Data Science, E-learning and Information Systems 2019 (Data\’19, Dubai, Arab Emirates, Dec. 2019, http://iares.net/Conference/DATA2019).


Suggested topics include:

  • Big Data Fusion
  • Big Image Fusion
  • Data Fusion in Incomplete or Imprecise Environments
  • Data Fusion in Distributed Environments
  • Data Fusion Algorithms
  • Data Fusion Architectures
  • Data Fusion for Time Series Analysis
  • Data Fusion in the Internet of Things
  • Data Fusion in Data Mining Tasks
  • Data Fusion in Sensors Networks
  • Image Data Fusion
  • Bio-inspired Data Fusion
  • Data Fusion in Environments with Limited Resources
  • Multi-agent Data Fusion Systems
  • Data Fusion Applications: Medicine, Education, Transportation, Economics, Robotics, etc.
  • Mining Big Data Fusion
  • Multimedia Big Data Fusion

Submission Guidelines:

Unpublished manuscripts or extended versions of papers presented at the conference are welcome. All submissions must not be currently under review for publication elsewhere. Conference papers may only be submitted if the paper was completely re-written or substantially extended (50%). For additional questions please contact the guest editors. All submitted papers will be peer reviewed using the normal standards of CAEE. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue.

Selected authors will be invited to submit their through the journal\’s web submission tool at evise.com/profile/#/COMPELECENG/login by selecting “SI-bdf” from the “Issues” pull-down menu during the submission process. Before submission, authors should carefully read the journal\’s Author Guidelines available at http://www.elsevier.com/wps/find/journaldescription.cws_home/367/authorinstructions


Submission of manuscript: Feb. 15, 2020

First notification: May 28, 2020

Submission of revised manuscript: June 28, 2020

Notification of the re-review: July 28, 2020

Final notification: August 6, 2020

Final paper due: September 30, 2020

Publication: Jan. 2021

Guest Editors:

– Dr. Shadi A. Aljawarneh (Main contact)

Prof. of Software Engineering at Jordan University of Science and Technology. Editor of IJCAC, IGI-Global, USA; ACM Senior Member.

Email: saaljawarneh@just.edu.jo

– Dr. Juan Alfonso Lara Torralbo

Prof. at Engineering School, UDIMA Universidad a Distancia de Madrid, Spain.

Email: juanalfonso.lara@udima.es

Shadi A. Aljawarneh is a full professor, Software Engineering, at the Jordan University of Science and Technology, Jordan; visiting professor, Concordia University, Montreal, Canada. He holds a BSc degree in Computer Science from Jordan Yarmouk University, a MSc degree in Information Technology from Western Sydney University and a PhD in Software Engineering from Northumbria University-England. He worked as an associate professor in faculty of IT in Isra University, Jordan since 2008. His research is centered in software engineering, web and network security, e-learning, bioinformatics, Cloud Computing and ICT fields. Aljawarneh has presented at and been on the organizing committees for a number of international conferences and is a board member of the International Community for ACM, Jordan ACM Chapter, ACS, and IEEE. A number of his papers have been selected as \”Best Papers\” in conferences and journals.

Juan A. Lara is Associate Professor and Research Scientist at Madrid Open University, MOU, Spain. He is currently Head of Department of Computer Science and Director of the Group of Research in Knowledge Management and Engineering. He is author of more than five online education books. He holds a Ph.D. in Computer Science and two Post Graduate Masters in Information Technologies and Emerging Technologies to Develop Complex Software Systems from Technical University of Madrid, Spain. He has published some book chapters and papers on several international conferences, and taken part in national and international research projects. He is author of more than a dozen papers published in international impact journals. His research interests in computer science include data mining, knowledge discovery in databases, data fusion, artificial intelligence and e-learning.

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