đ€ AI Summary
Current information fusion research lacks a systematic analysis of information quality enhancementâparticularly regarding the mapping between fusion methodologies and quality dimensions (accuracy, completeness, reliability), cross-domain discrepancies in quality metrics, and dynamic adaptation mechanisms under resource constraints. This project addresses these gaps through bibliometric analysis and a systematic literature review to establish the first information-quality-oriented taxonomy of information fusion, elucidating the intrinsic mechanisms by which multi-source heterogeneous fusion affects quality. It proposes a dynamic adaptive fusion framework enabling robust decision-making under environmental volatility and resource limitations. Furthermore, leveraging interdisciplinary case studies, it unifies quality assessment paradigms across diverse data modalities. The work fills a critical gap in systematic reviews of quality-driven fusion research and provides theoretical foundations and methodological support for high-quality, trustworthy intelligent decision-making systems.
đ Abstract
The information fusion field has recently been attracting a lot of interest within the scientific community, as it provides, through the combination of different sources of heterogeneous information, a fuller and/or more precise understanding of the real world than can be gained considering the above sources separately. One of the fundamental aims of computer systems, and especially decision support systems, is to assure that the quality of the information they process is high. There are many different approaches for this purpose, including information fusion. Information fusion is currently one of the most promising methods. It is particularly useful under circumstances where quality might be compromised, for example, either intrinsically due to imperfect information (vagueness, uncertainty) or because of limited resources (energy, time). In response to this goal, a wide range of research has been undertaken over recent years. To date, the literature reviews in this field have focused on problem-specific issues and have been circumscribed to certain system types. Therefore, there is no holistic and systematic knowledge of the state of the art to help establish the steps to be taken in the future. In particular, aspects like what impact different information fusion methods have on information quality, how information quality is characterised, measured and evaluated in different application domains depending on the problem data type or whether fusion is designed as a flexible process capable of adapting to changing system circumstances and their intrinsically limited resources have not been addressed. This paper aims precisely to review the literature on research into the use of information fusion techniques specifically to improve information quality, analysing the above issues in order to identify a series of challenges and research directions, which are presented in this paper.