Pervasive, domain and situational-aware, adaptive, automated, and coordinated big data analysis, contextual learning and predictive control of business and operational risks and security

A computer-implemented method, comprising:

• providing a processor-implemented self-similar structure comprising a plurality of monitored and controlled elements (MCE) for each of a plurality of networks representing their logical and physical interconnection;
• deploying security and operation data and transaction monitoring, acquisition, analysis, learning, prediction, and inference pervasively around each MCE to monitor, analyze, and learn about said MCE's structural connections and functional interactions with other MCEs, said security monitoring providing situational intelligence and computing short and long term risk control decisions and security posture information from each producing MCE with other consuming MCEs;
• based upon said self-similar structure, monitoring, analyzing, learning, and predicting security and operational risk state;
• pervasively monitoring security and operational data and patterns and their interconnection, interaction, relation and network features extracted from said network, and adaptively reconfiguring security and operation control capabilities, capacities, and operational parameters, ranges and thresholds at selected hierarchical levels and at selected time scales in response to enterprise situational knowledge;
• providing an operational security and risk analysis engine algorithmically processing, learning, and correlating elemental, systemic, and cross-domain situational intelligence, and cross-correlating data sets and corresponding networks to logically and structurally infer and mathematically predict, validate, rank, and order situational operation security;
• inferring and producing a dynamic decision output based on the operational policies, process and rules, constraints, configurations, trigger parameters, and ranges for implemented rules and processes for reporting and controlling enterprise operation and security risk management;
• providing real-time and dynamic input back into coordinated learning and updating of said situational intelligence, situational knowledge, and domain knowledge.

A computer-implemented networked business risk and operational security big data driven analysis, learning, prediction and control method, comprising:

• providing an enterprise-wide computer network;
• and
• providing a plurality of clusters of subsystems in said network, each subsystem comprising a plurality of lower level subsystems and individual computers and data analysis, learning, prediction and control applications, said individual computers and data analysis, learning, prediction and control applications, in turn, comprising a plurality of other smaller monitored and controlled elements (MCE) comprising any of computers, sensors, data acquisition, storage, analysis, learning, and business risk and operational security prediction and control systems at every level within the enterprise-wide computer network, and underlying business processes;
• wherein each cluster comprises one or more computers designated as a server or client, wherein said computers within each cluster communicate with each other through physical network configurations and logical messaging structures, wherein a computer comprises any of a real computer and a virtual computer;
• and
• providing a conceptual and computational model of situational knowledge, business risk and security control knowledge in a formal, machine-interpretable form comprising a combination of tabular data set and node-and-edge graphs representing the corresponding network;
• wherein columns in the table and nodes in the graphs represent any of monitored data, transaction values, message content and meta-data, operational events, and security activity comprising situational data as well as business risk and operational security control policy and rule related facts and data elements;
• wherein edges in the graph represent structural connection and functional interaction among situational data elements as discovered in the data, transaction, and exchanged messages as well as known structural connections, correlations and dependencies among facts and data elements representing risk control policies and security rules;
• wherein nodes have attributes and values representing properties of the elements representing the node as well as attributes and values comprising thresholds and ranges representing properties of elements comprising risk control policy and security rule nodes;
• wherein nodes comprise any of multiple types, said types comprising any of suppliers, raw materials, customers, finished products, production operators, manufacturing machines representing the situational knowledge about supply chain or physicians, patients, lab tests, diagnosed diseases, prescribed medications, all representing situational knowledge about clinical work flow or payment processing flow in a hospital business;
• wherein node attribute values are found or discovered in transactions, messages, data patterns, network features and statistical derivation;
• wherein edges have label attributes representing relations and interconnections between nodes as well as dependencies among risk control policy and security rule nodes, and values representing statistical probability, strength of relationships, threshold of dependency and frequency of interactions as discovered in data, transactions, and exchanged messages;
• and
• wherein multiple edges exist between a same pair of nodes representing different types of relations, dependencies and interactions between the nodes as discovered in the data, both structural and functional;
• and
• providing a conceptual and computational model of domain knowledge comprising any of structural and correlational data patterns between any of goods and raw material, geo location and lead time of suppliers, lead time and inventory holding cost, machine failure and on-time delivery miss, and market factors and demand pull variance by customers for supply chain business risk analysis or between seasonality, patient age, and patient volume and admission causes in a hospital, between physician specialty, disease diagnosis accuracy, and clinical cost and outcome for clinical operation risk analysis.