FORECAST: Next-Generation Automated Indications & Warnings

D. Perez
Black Cape, Inc., Virginia, United States

Keywords: Publicly available information, indications and warnings, all-domain awareness, event detection/correlation/search/visualization

Fusion of Open Source Resources for Event Classification and Alerting of Significant Threats (FORECAST), Black Cape’s next-generation, automated indications and warnings solution uses all domain data to reduce cognitive overload by detecting operationally relevant patterns across diverse data sets and to alert operators/analysts. Through a SBIR Phase I R&D study of publicly available information (PAI) collection management, we evaluated the feasibility of converting raw PAI into a set of discrete, relevant events. This research focused on core capabilities required to enable data ingest, data storage, data enrichment, event detection, event correlation, event search, and event visualization. We identified and evaluated approaches to developing/integrating these capabilities and relevant data sources that may be processed by the system and designed FORECAST to perform key operations required to convert raw PAI into discrete, correlated events. To demonstrate this approach's feasibility and reduce future execution risk, we developed proof-of-concept capabilities that make up the key system functionality and resulted in a proof of concept system that converted raw PAI into discrete, searchable events containing information about event category, location, date/time, entities involved, and more. Based on the research Black Cape conducted and the demonstrated success of the proof of concept system.