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Data Science
Time Complexity, Inferential Uncertainty, and Spacekime Analytics
Anglais
222 b
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This book presents spacekime analytics which addresses the challenges of interrogating Big Data by transforming the notion of multiple samples, acquired in the 4D Minkowski spacetime, into a 5D spacekime extension manifold. This embedding of the 4D space into a 5D spacekime manifold facilitates a new kind of data analytics, which naturally reduces to its classical 4D spacetime analogue associated with unobserved kime-phases (timedirections). The book uses an alternative data representation based on complexifying time and lifting the classical 4D universal spacetime to a 5D spacekime manifold. The rationale for this extension is multifold. The spacekime representation allows us to resolve some of the problems of time, define mathematical equations, and derive analytical expressions describing the natural laws of physics that agree with their standard 4D counterparts. In addition, the authors demonstrate that careful data representation, reconstruction, modeling, and analytics in the extension spacekime manifold could potentially expose valuable information that can enhance observation-based scientific inference, data-driven predictions, and evidence-based decision-making processes.
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