Advantages and Challenges of Information Fusion Technique for Big Data Analysis: Proposed Framework

Elham  Nazari; Rizwana  Biviji; Amir Hossein  Farzin; Parnian   Asgari; Hamed  Tabesh

doi:10.18502/jbe.v7i2.6737

Elham Nazari Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran.
Rizwana Biviji Science of Healthcare Delivery, College of Health Solutions, Arizona State University, Phoenix, AZ, USA
Amir Hossein Farzin Department of Computer Engineering, Ferdowsi University, Mashhad, Iran.
Parnian Asgari Department of Health and Information Technology, Mashhad University of Medical Sciences, Mashhad, Iran.
Hamed Tabesh Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran.

DOI: https://doi.org/10.18502/jbe.v7i2.6737

Keywords: Information fusion; Symbol fusion; Decision fusion; Big data; Data mining

Abstract

Introduction: Recently, with the surge in the availability of relevant data in various industries, the use of Information Fusion technique for data analysis is increasing. This method has several advantages, such as increased accuracy, and the use of meaningful information. In addition, there are certain challenges, including the impact of data type and analytical method on results. The goal of this study is to propose a framework for introducing the advantages and classifying the challenges of this technique.

Method: We conducted a review of articles published between January 1960 and December 2017 for the design stage and from January 2018 to December 2018 for the evaluation stage. Articles were identified from various databases such as Science Direct, IEEE, Scopus, Web of Science, and Google Scholar, using the keywords decision fusion, information fusion, and symbolic fusion. We report the advantages and challenges of the methodologies described in these articles. Analysis was conducted in accordance with PRISMA guidelines.

Results: A total of 132 articles were identified in the design stage and 90 articles were identified in the evaluation stage. Categories within the framework for challenges include “hardware and software requirements for processing and maintaining the process”, “data” and “data analysis method”. The categories for advantages include “value modeling”, “preferable management of uncertainty and variability”, “excellent decision making”, “comprehensive interpretation and representation”, “data management” and “simplicity of infrastructure”. Our results indicate using these two frameworks with 95% Confidence interval.

Conclusion: An overall understanding of the advantages and challenges of the information fusion technique could act as a guide for the researcher for the correct usage of this technique.