Vector Logic Analysis of Big Data

The paper is focused on identification of patterns and objects using keywords and data. The following problems are considered: searching for object identifiers based on a truth table, where keywords are formed in columns that determine the similarity and difference of objects in the flow; defining the equivalence classes of keywords based on the analysis of truth table columns, built on a digitized data flow; generating a business process model in the form of a logical vector based on a binary matrix of patterns; using a business process model to verify the actions of operators performing regulatory functionality at the enterprise. Transfer of the von Neumann architecture into memory and replacing the powerful read-write processor with logical vector transactions to reduce energy and time costs when processing big data is proposed. Smart data structures and simple algorithms based on read-write transactions for in-memory big data as address computing are being developed.