Big Data Analysis From the Perspective of Fully Actuated System

In recent years, big data analytics technologies have encountered common challenges in processing multimodal heterogeneous data, including dimensional redundancy, modality barriers, and insufficient model generalization. These issues urgently require interdisciplinary theoretical support to overcome current bottlenecks. Notably, fully actuated systems have garnered increasing attention due to their distinctive characteristics of iterative elimination, independent degrees of freedom, and strong robustness. We observe that these properties hold significant potential for integration with big data analytics to address the aforementioned challenges. Specifically, this paper systematically reviews the development of high-order fully actuated system theories in control engineering and explores their potential for cross-domain migration to complex data analytics. Research demonstrates that existing works, through the introduction of core concepts such as independent degrees of freedom control and iterative elimination of redundancy, provide novel approaches to resolve critical issues including high-dimensional data representation and cross-modal correlation modeling. Furthermore, this study delineates technical pathways for heterogeneous data analytics across two emblematic application domains, healthcare and industrial monitoring, revealing inherent synergies between fully actuated systems and data science disciplines. The incorporation of fully actuated system principles effectively addresses challenges related to dimensional redundancy, modality barriers, and limited model generalization. Additionally, we discuss potential extensions in dynamic system modeling and real-time feedback mechanisms, outlining promising research directions for future exploration.