Next Article in Journal
A Framework Using Contrastive Learning for Classification with Noisy Labels
Previous Article in Journal
APIs for EU Governments: A Landscape Analysis on Policy Instruments, Standards, Strategies and Best Practices

Information Quality Assessment for Data Fusion Systems

Instituto Tecnológico Metropolitano, Cra. 74d #732, Medellín 050034, Colombia
Facultad de Ciencias Básicas, Universidad de Medellín, MATBIOM, Cra. 87 #30-65, Medellín 050010, Colombia
Modeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Ben Guerir 47963, Morocco
Faculty of Engineering, Corporación Universitaria Autónoma de Nariño, Carrera 28 No. 19-24, Pasto 520001, Colombia
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Juanle Wang
Received: 29 April 2021 / Revised: 3 June 2021 / Accepted: 3 June 2021 / Published: 8 June 2021
(This article belongs to the Section Information Systems and Data Management)
This paper provides a comprehensive description of the current literature on data fusion, with an emphasis on Information Quality (IQ) and performance evaluation. This literature review highlights recent studies that reveal existing gaps, the need to find a synergy between data fusion and IQ, several research issues, and the challenges and pitfalls in this field. First, the main models, frameworks, architectures, algorithms, solutions, problems, and requirements are analyzed. Second, a general data fusion engineering process is presented to show how complex it is to design a framework for a specific application. Third, an IQ approach, as well as the different methodologies and frameworks used to assess IQ in information systems are addressed; in addition, data fusion systems are presented along with their related criteria. Furthermore, information on the context in data fusion systems and its IQ assessment are discussed. Subsequently, the issue of data fusion systems’ performance is reviewed. Finally, some key aspects and concluding remarks are outlined, and some future lines of work are gathered. View Full-Text
Keywords: context assessment; data fusion; information quality; quality assessment context assessment; data fusion; information quality; quality assessment
Show Figures

Graphical abstract

MDPI and ACS Style

Becerra, M.A.; Tobón, C.; Castro-Ospina, A.E.; Peluffo-Ordóñez, D.H. Information Quality Assessment for Data Fusion Systems. Data 2021, 6, 60.

AMA Style

Becerra MA, Tobón C, Castro-Ospina AE, Peluffo-Ordóñez DH. Information Quality Assessment for Data Fusion Systems. Data. 2021; 6(6):60.

Chicago/Turabian Style

Becerra, Miguel A., Catalina Tobón, Andrés E. Castro-Ospina, and Diego H. Peluffo-Ordóñez 2021. "Information Quality Assessment for Data Fusion Systems" Data 6, no. 6: 60.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop