Why Do Big Data and Machine Learning Entail the Fractional Dynamics?

Fractional-order calculus is about the differentiation and integration of non-integer orders. Fractional (FC) based on fractional-order thinking (FOT) has been shown to help us understand complex systems better, improve processing signals, enhance control systems, increase performance optimization, even extend enabling potential for creativity. In this article, authors discuss fractional dynamics, FOT rich stochastic models. First, use dynamics in big data analytics quantifying variability stemming from generation justified. Second, we show why needed machine learning optimal randomness when asking: “is there a more way optimize?”. Third, an case study configuration network (SCN) machine-learning method with heavy-tailed distributions discussed. Finally, views (physics-informed) future research are presented concluding remarks.