Bibliometric analysis reveals trends and challenges in intelligent learning for multidimensional data streams, highlighting interdisciplinary applications and international collaboration.
Intelligent learning applied to multidimensional data streams has established itself as a rapidly expanding field, driven by the growth of ubiquitous computing and the Internet of Things. The complexity of these streams, characterized by their high dimensionality, variability, and continuous nature, poses significant challenges for traditional approaches to analysis. This study presents a bibliometric analysis of scientific output indexed in Scopus between 2015 and 2025, with the aim of identifying trends, challenges, and opportunities in this field. The results show sustained growth in publications, a marked interdisciplinary orientation, and a diversity of applications including transportation, biomedicine, energy, and information systems. Likewise, there is a geographical concentration in certain leading countries and uneven development in terms of international collaboration. This work contributes to mapping the current state of the field and points to future lines of research aimed at its consolidation.
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Reyes et al. (2025) studied this question.
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