Large and complex data theory
Large and complex data theory research focuses on the mathematical and statistical foundations required to analyze massive and intricate datasets. This field addresses big data challenges arising from diverse sources and complex data structures, offering essential tools for accurate and efficient data analysis. As a vital subfield within MATHEMATICAL SCIENCES > Statistics, it supports advancements across disciplines relying on large-scale data. JoVE Visualize enhances this research by pairing PubMed articles with JoVE’s experiment videos, providing a richer and more practical understanding of research methods and experimental findings.
Established approaches within large and complex data theory commonly include dimensionality reduction, advanced statistical modeling, and scalable algorithm design. Techniques such as principal component analysis, clustering, and hierarchical modeling help manage the characteristics of big data like volume, variety, and velocity. These methods facilitate reliable big data analysis by addressing noise, heterogeneity, and correlation structures inherent in large datasets. Researchers often draw on mathematical tools to characterize and quantify data complexity, providing foundational frameworks for interpreting diverse big data examples.
Recent advances in the field emphasize machine learning integration, adaptive algorithms, and distributed computing to handle increasingly complex data environments. Methods exploring real-time data streams, tensor decompositions, and nonlinear dimensionality reduction are growing in importance. Innovative approaches also focus on addressing new big data challenges posed by heterogeneous data sources and multimodal data integration. These developments expand the scope of large and complex data theory by enabling more flexible, scalable, and interpretable analysis strategies that meet evolving research needs.
K K Lan, J T Wittes
N C Andreasen, S Arndt, T Cizadlo, D S O'Leary, G L Watkins, L L Ponto, R D Hichwa