Abstract
The paper presents new principles of multidimensional data image generation for monitoring the behavior of complex systems by means of cognitive machine graphics. These images create in the mind of the human operator cognitive spectacular images that reveal the topological features of multidimensional data structures. Such images have an aesthetic appeal and stimulate human intuition in relation to the subject interpretation of the properties of complex systems that gave rise to this data. In other words, when these images are perceived, the human operator is able to identify individual geometric properties of the observed image and associate them with the subject content of the processed multidimensional data. Fundamentally new algorithmic approaches of cognitive visualization based on the representation of a set of points in multidimensional Euclidean space as a topological map in the form of simplices and polyhedra are proposed. Such a partition of Euclidean space into simplices allows revealing topological invariants hidden for the operator in multidimensional data, which in a certain sense determine the key characteristics of the behavior of complex systems. It is very important to combine the proposed cognitive technology with the modern capabilities of intelligent software interfaces and multidimensional statistical data analysis programs.
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