СLASSIFIER OF BIG DATA OBJECTS OF THE SOCIO-ECONOMIC SYSTEM

Abstract

The well-known models that provide the synthesis of object classifiers are analyzed: the Grothendieck topos model, Kripke’s intuitionistic model and the model of the normal Markov algorithm, in which similar approaches to the representation of objects are highlighted. The differences in the classifiers used in them are highlighted. A categorical-theoretical model of topos has been developed, in which sets are represented by n-categories. A generalized classifier has been developed that includes the properties of the Grothendieck, Kripke and Markov classifiers. An N-scheme is synthesized to replace the g-scheme of the normal Markov algorithm. A model of n-category topos has been developed and theoretically substantiated, in which the compositions of morphisms of an object are specified by an N-scheme. The alphabet of the n-category associator is synthesized, which provides the representation of non-associative compositions of morphisms, interacting objects, taking into account the assumptions laid down in the Kripke model. A database has been developed that implements the synthesized topos model. It is noted that such a database can be implemented as a cascade of unordered containers with the computation of hash functions to quickly find and retrieve keys and values. For some keys, such a base, it is allowed to duplicate a key in containers. The database provides a synthesis of self-learning on the input sampling of artificial intelligence data and supported by control lists of objects of selection, exclusion and deletion. The normal Markov algorithm is adapted to the theoretical model of the Grothendieck topos, taking into account the assumptions of the Kripke model, by replacing the g-scheme with the synthesized N-scheme.