Intelligent Computing for Cybersocial Space

Intelligent computing architectures are proposed for managing cyber social processes based on monitoring and analysis of big data. It is proposed to process big data as truth table addresses to solve problems of identification, clustering, and classification of patterns of social and production processes. A family of automata is proposed for analyzing big data such as addresses. The purpose of this investigation is to enhance the big data economy by analyzing data as truth table addresses to identify patterns of production functionality based on the similarity-difference metric. The truth table is considered as a smart form of explicit data structures that have a useful constant the standard order of addresses. For processing big data, it is necessary to structure their using a truth table. The representation of data as addresses is associated with the unitary coding of patterns by binary vectors on the found universe of primitive data. The mechanism is focused on non-processor data processing based on read-write transactions using in-memory computing technology with significant savings in time and energy. The truth table metric for big data processing is parallelism, technological simplicity, and linear computational complexity. The tradeoff for these benefits is the exponential memory redundancy of storing explicit structured data.