THE USE OF MARKOV ALGORITHMS FOR THE STUDY OF l-VOIDS IN BIG DATA OF SOCIO-ECONOMIC SYSTEMS. Part 1

Abstract

A synthesized N-scheme of the Markov algorithm is considered, which can be replaced by his wellknown γ-scheme. Using the N-scheme of the Markov algorithm, the properties of l-voids of big data of socio-economic systems are investigated as an element of the typology of the risk of money laundering. In the N-scheme, the numbers-types w in the alphabet M = litj/abdgckm of words in the Markov alphabet A that make up the l-voids are determined. The alphabet M is considered as a formal grammar consisting of terminal litj and non-terminal abdgckm symbols. Examples of l-voids in the form of a simple square cycle and an incomplete Euler cycle are considered. Examples of filling in the artificial intelligence database for l-voids are given in order to conduct an independent experiment on checking the N-scheme of the Markov algorithm. It is proposed to represent l-voids of big data of socio-economic systems in the form of a diagram of occurrences of words in the Markov alphabet А and subject them to analysis. The analysis of occurrence diagrams is proposed to be carried out using the N-scheme of the Markov algorithm. In addition, we present the results of the analysis of the l-voids of a simple square cycle with heterogeneous data, as well as the analysis of its locally lh-final occurrences and under conditions where it is impossible to verify morphisms in time. It is concluded that the study of methods for applying hashing in conjunction with the N-scheme of the Markov algorithm, in order to determine the most appropriate hash function and algorithm for its implementation, is a promising scientific task. It is expedient to synthesize a specialized data container using hash functions and hash tables for the N-scheme of the Markov algorithm. It is indicated that for verification, it is possible to implement the N-scheme of the Markov algorithm using hash functions and hash tables known in modern programming languages.