Information Quality Assessment for Data Fusion Systems

Abstract

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.

Determining Information Quality in ICT Systems

“…In the following sections, in addition to the description of the method, an example of calculations for the selected model and the simulation of the model results depending on the input coefficients are also presented. A similar approach was presented by the main co-author in [7,[26][27][28][29]. Applying the presented approach to the assessment and analysis of other systems by assessing reliability or risk, as presented in the works [30][31][32], is also expected.…”

Integrated Video and Acoustic Emission Data Fusion for Intelligent Decision Making in Material Surface Inspection System

“…When acquiring new knowledge about information processes, data fusion is associated with automated methods for extracting meaning from incoming information, selecting algorithms and methods, evaluating information quality, and evaluating information fusion systems. The evaluation of information fusion systems [ 39 ] is seen as the biggest challenge; there are still no standard metrics used, so researchers develop their own metrics. We emphasize that the main goal of data consolidation systems always remains the same—to obtain better information about an object, process, or phenomenon, using data from multiple sources.…”

Effect Of the Disclosure of Corporate Social Responsibility Practices on The Financial Value of The Companies Listed on The Colombian Stock Exchange

“…As future work, it is proposed to extend studies of social responsibility not only to the business sector but also to higher education institutions and analyze intellectual capital (Londoño et al, 2021) and its influence on social responsibility. In addition, a framework based on JDL data fusion model and information quality could provide a comprehensive assessment of social responsibility with traceability of information, and situation and impact/risk assessment taking into account data quality (Becerra et al 2018;Becerra et al 2021).…”