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.