Special Section on Recent Advancements in Big Data Fusion
• 大类 : 工程技术 - 4区
• 小类 : 计算机：硬件 - 3区
• 小类 : 计算机：跨学科应用 - 4区
• 小类 : 工程：电子与电气 - 4区
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.