CEDAR (Complex Event Discovery, Analysis, and Ranking)

21CT and CUBRC propose CEDAR (Complex Event Discovery, Analysis, and Ranking), a robust framework to detect and analyze indicators of complex activities, such as an insurgent ambush observable via myriad of simple events in multiple sensor streams. CEDAR will provide a mapping from complex behavior, such as conducting an insurgent ambush or rebuilding trust in local communities, to simple event indicators detectable in intelligence, open-source, blue force, and population sensor streams. Examples of such events include increased chatter in blue or red force networks, changes in population sentiment, or curfew movement. CEDAR will also provide an integrated process to execute event queries that leverages our team’s abilities in performing approximate pattern matching over multi-dimensional data. This capability enables us to mine sensor feeds at scale, such as detecting motion in video, non-verbal audio cues, such as gunfire, sentiment in text sources, and changes in human network activity. Finally, CEDAR will provide a suite of event projections to facilitate discriminating aggregate events that indicate complex activity. By combining event projections with semantic event scoring, we can assess if detected events are progressing on a “good” or “bad” vector and exploit this information to choose actions to modulate complex event behavior.