Fusion from Big Data to Smart Data

It has become increasingly common in science and technology to gather data about systems at different levels of granularity or from different perspectives. With the emergence of Smart City, plethora of data sources have been made available for wide variety of applications. Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity.

Smart data aims to filter out the noise data and produce valuable data, which can be effectively used by enterprises and governments for planning, operation, monitoring, control, and intelligent decision making. Although unprecedentedly large amount of data can be available with the advancement of advanced data fusion techniques, the key is to explore how big data can become smart data and offer intelligence. Advanced big data modeling and analytics are indispensable for discovering the underlying structures from fused data and further acquiring smart data.

The aim of this theme issue is to explore the state-of-the-art, methodologies and applications related to all aspects of Fusion from big data to smart data. Review or summary articles — for example a critical evaluation of the state of the art, or an insightful analysis of established and upcoming technologies — may be accepted if they demonstrate academic rigor and relevance.

Topics of interest include (but not limited to)

· Big Data Fusion

· Data Fusion Algorithms

· Data Stream processing for Data Fusion

· AI for Data Fusion

· Data Fusion in the Internet of Things

· Bio-Inspired Data Fusion

· Data Fusion Applications: Medicine, Economics, Cultural Heritage, etc.

· Multimedia Data Fusion

· Analytics for Data Fusion

· Data Visualizations techniques for Data Fusion

Please prepare your paper along with all the supplementary materials for your submission. The papers submitted to this special issue must be original. Besides that, they must not be published, “under review”, or even be submitted in any other journal, conference, or workshop. Papers will be peer-reviewed by at least three independent reviewers and will be chosen based on contributions including their originality, scientific quality as well as their suitability to this special issue. The journal editors will make the final decision on which papers will be accepted.

Authors must ensure that you carefully read the guide for authors before submitting your papers. The guide for authors and link for online submission is available on the Information Fusion homepage at: https://www.journals.elsevier.com/information-fusion. Please select “SI: FBDSD” when you reach the “Article Type” step when submitting your papers. For any inquiry or question regarding this special issue, authors may contact directly via email to Laurence T. Yang at ltyang@stfx.ca.

Guest Editor(s)

Laurence T. Yang, St Francis Xavier University, Canada

Email: ltyang@stfx.ca

Francesco Piccialli, University of Naples FEDERICO II, Italy

Email: francesco.piccialli@unina.it

Xiaokang Zhou, Shiga University, Japan

Email: zhou@biwako.shiga-u.ac.jp

Deadline for Submission: December 1. 2020