МЕТОДЫ ИНФОРМАЦИОННОГО ПОИСКА НЕСТРУКТУРИРОВАННЫХ ДАННЫХ

Abstract

The article discusses new methods used to solve the problem of information retrieval of unstructured (text) data. Search for documents is carried out by keywords in the natural language used in search engines. Thus, on the basis of polynomial algorithms, a universal multi-key sampling machine is created with improved time and space characteristics. This proposed machine can be used for processing big data in various areas of the economy. To achieve this goal, the new poly-nomial algorithms are based on the problem of the sum of subsets, which belongs to the NP-complete class. These algorithms are significantly more time and space efficient than the existing best polynomial and exponential algorithms.