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.
Highlights
The evolution of information has generated an enormous amount of heterogeneous data
Some frameworks or methodologies were not included for different reasons such as because they were not found in our search or because we considered them to be very similar to others
The methodology was validated in two simulated environments; the first is an automatic target recognition that uses data obtained from radar and infrared-electro-optical sensors to identify the target as well as the friend or foe provided by the operators
Summary
The evolution of information has generated an enormous amount of heterogeneous data This could negatively affect information systems because it requires major efforts from them to appropriately address Information Quality (IQ) problems and to avoid the deterioration of their underlying IQ. Information is collected from multiple and diverse sources (e.g., web-based and human-provided systems) that are naturally multisensorial and, highly conflicting [1] This is why their corresponding acquisition processes involve different time rates and conditions, and various experiments of the target or phenomenon, which results in different data types. These data can broadly be classified into two big classes: hard and soft [2]. There is a wide range of issues and matters on how to properly measure information systems’ performance [6,7], which is considered one of the most complex stages during information processing
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