Abstract: The present invention addresses deficiencies of the art with respect to collaborative computer networks consisting of mixed data, control functions, analysis functions, and sensors in complex systems of systems. The method involves a database framework for representing complex heterogeneous characteristics of processes, systems, and systems of systems that feature many to many interrelationships. The homoiconic graph framework takes the form of an executable graph database, which is often faster for associative data sets, and maps more directly to object-oriented computer applications for large-scale operations. The invention provides a method to execute the graph database, in that it comprises nodes that are both data fragments and executable components. It is characterized as a homoiconic or executable graph framework to distinguish this unique feature from the concept of a graph database, which generally is a repository of connected data only.

Abstract: The present invention addresses deficiencies of the art with respect to collaborative computer networks consisting of mixed data, control functions, analysis functions, and sensors in complex systems of systems. The method involves a database framework for representing complex heterogeneous characteristics of processes, systems, and systems of systems that feature many to many interrelationships. The homoiconic graph framework takes the form of an executable graph database, which is often faster for associative data sets, and maps more directly to object-oriented computer applications for large-scale operations. The invention provides a method to execute the graph database, in that it comprises nodes that are both data fragments and executable components. It is characterized as a homoiconic or executable graph framework to distinguish this unique feature from the concept of a graph database, which generally is a repository of connected data only.