This study on enhancing investigate pattern detection through inexact matching and graph databases demonstrates the capability of detecting individuals/groups meeting query criteria as well as the iterative query performance in graph databases.
Results of this study on enhancing investigate pattern detection through inexact matching and graph databases demonstrate the capability of detecting individuals/groups meeting query criteria as well as the iterative query performance in graph databases. Tracking individuals or groups based on their hidden and/or emergent behaviors is an indispensable task in homeland security, mental health evaluation, and consumer analytics. On-line and off-line communication patterns, behavior profiles and social relationships form complex dynamic evolving knowledge graphs. Investigative search involves capturing and mining such large-scale knowledge graphs for emergent profiles of interest. While graph databases facilitate efficient and scalable operations on complex heterogeneous graphs, dealing with incomplete, missing and/or inconsistent information and need for adaptive querying pose major challenges. The authors address these by proposing an inexact graph pattern matching method, which is implemented in a graph database with a scoring mechanism that helps identify hidden behavioral patterns. PINGS ( P rocedures for IN vestigative G raph S earch), a graph database library of procedures for investigative graph search is presented. The authors evaluate this approach on three datasets: a synthetically generated radicalization dataset, a publicly available patient’s ICU hospitalization stays dataset, and a crime dataset. These varied datasets demonstrate the wide-range applicability and the enhanced effectiveness of observing suspicious or latent trends in investigative domains. (Published Abstract Provided)
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