Precedence tree guided search for the efficient identification of multiple situations of interest – AND/OR graph matching

• Presents method for modeling multiple situations of interest as single template graph.
• Enables precedence tree guidance of a graph matching (GM) search heuristic.
• Demonstrates significant speedup of the proposed AND/OR GM methodology.
• Performs numerous numerical experiments investigating other AND/OR GM policies.
• Describes intelligence analysis applications for the proposed techniques.

Intelligence analysis is a domain characterized by a torrent of streaming data within which a very small portion contains useful knowledge or actionable intelligence. Intelligence analysts have to sift through the compiled data and weave through a complex web of convoluted connections in an attempt to illuminate information requirements (IR) and maintain situational awareness. Automated methodologies have eased the manual burden of this process to some extent. Data are naturally modeled in a graphical form representing the known people, places, events and the relationships between them. Graph matching algorithms in which an information requirement is formulated as a template graph or situation of interest to be found in the observed data graph have been successfully employed in intelligence analysis processes. Absent from these past contributions is the recognition that partial information requirements, such as indicators and warnings, are not mutually exclusive to a specific IR, and an understanding of the characteristics of the underlying data can lead to significant performance benefits. The knowledge of overlapping template sections forms the motivation for precedence tree guided search and AND/OR templates. Through the recognition of the overlapping sections, a single AND/OR template can be created to answer many information requirements. This paper presents a novel algorithm for the intelligent traversal of an AND/OR template, providing increased algorithmic efficiency over the execution of multiple sequential graph matching instances. This paper focuses on development of an algorithm for intelligent AND/OR template traversal with computational results illustrating the effectiveness of the developed methods. The results indicate a significant improvement in runtime (with a speedup over 5 in some cases) while maintaining a good solution quality (within 2% of multiple AND path graph matching executions) in AND/OR and precedence tree guided graph matching.