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The progression of big data analytics framework must be clearly understood so that novel approaches can be developed to advance this state-of-the-art discipline. One of the current research efforts in big data analytics is the integration of deep learning and Bayesian optimization, which can help the automatic initialization and optimization of hyper-parameters of deep learning and enhance the implementation of iterative algorithms in software. Therefore, it is appropriate to introduce a new research topic—transformative knowledge discovery—that provides a research ground to study and develop smart machine learning models and algorithms that are automatic, adaptive, and cognitive to address big data analytics problems and challenges.

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This page is a summary of: Big data analytics: Machine learning and Bayesian learning perspectives—What is done? What is not?, Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, September 2018, Wiley, DOI: 10.1002/widm.1283.
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