NUMBERS-TYPES OF THE CLASSIFIER OF BIG DATA OBJECTS OF THE SOCIO-ECONOMIC SYSTEM

Abstract

The N-scheme developed to replace the well-known g-scheme of the normal Markov algorithm is investigated. The numbers-types of the classifier of big data objects of socio-economic systems are introduced, theoretically substantiated and synthesized, which determine the N-scheme of the Markov algorithm. The purpose of such studies is determined – this is to find scientific and technical ways to counter money laundering, for which it is necessary to analyze a large amount of data on economic interactions (relationships) of individuals and legal entities that make up various socio-economic systems, where g-schemes are more expressive than the wellknown Markov g-schemes. It has been established that for this it is advisable to study the properties of combinations of initial, terminal and isomorphic morphisms (relationships) of objects of socio-economic systems. It was found that each word from the Markov alphabet A can be characterized by the morphism number n, which in turn can be represented as one of the letters of the alphabet M =tjilckmg, where its association method is defined by M, with the value f in the unit of measure “type”. It is noted that then for each object it is possible to collect data on all its initial, terminal, isomorphic relationships (interactions) and represent them in the form of w-syllables and w-words from letters of the alphabet M, completing the formation of the N-scheme of the Markov algorithm. Examples of categories and their type numbers are given, composed of letters of the alphabet M. It is noted that a certain lattice is formed from the type numbers, which can be used to solve NP-complete problems. It has been found that it is advisable to evaluate superlarge w-words of numbers-types, since they can contain fragments that are responsible for the entry of the algorithm into an infinite loop.