Big Data Driven Cyber Analytic System

An infrastructure for parallel analyzing big data in order to search, pattern recognition and decision-making based on the use of the Boolean metric of cyberspace measurement is proposed. It is characterized by using only logical xor-operation for determining the cyber-distance by means of cyclic closing at least one object, which allows significantly increasing the speed of analysis of large data. A new approach to vector-logical big data processing based on the total removal of arithmetic operations, which impact on the performance and hardware complexity, is offered, the approach can be efficiently implemented based on modern multiprocessor digital systems-on-chips and virtual parallel processors, operating under the control of cyber-physical systems or cloud service-filters. A qubit-vector model of computing automaton is proposed, it is characterized by the transactional interaction of memory components, which represent the combinational and sequential elements and are implemented in the form of a qubit or "quantum" primitives needed to create parallel virtual computers and cloud-focused processors. A new structural model for analyzing big data is developed, it is characterized by the use of cloud services, cyber-physical and search systems, virtual parallel multiprocessors with a minimal set of vector-logical operations for accurate information retrieval based on the proposed Boolean metric and non-numerical quality criteria, which makes it possible to create a semantic infrastructure of cyberspace by the competence classification and metric ordering big data across the cyber-ecosystem of the planet.