Existing USAF workflow requires fusion intelligence analysts to manually query multiple siloed databases, using a variety of disaggregated tool suites, in order to access varying types of data streams. Research workflows are generally categorized in four ways: (1)The analysts leadership identifies a need; (2) mission partner sites share a problem set; (3) a warfighter shares a problem set, or (4) an analyst personally recognizes a research need. Once an analyst identifies relevant information or a problem set to research, he/she begins manually combing through structured and unstructured databases (HUMINT, SIGINT, MASINT, OSINT, GRINTSUMs, etc) to corroborate potentially relevant information. Existing methodologies require the analyst to gather intelligence from multiple databases on separate domains, sift through the unstructured data and structured sources manually, and finally to corroborate information that will support an intelligence estimate or report. Furthermore, the analyst performs these tasks without an intuitive search platform that delivers relevant data to their searches and lacks a centralized geospatial information system that provides all of the information in a dashboard format. These deficiencies place significant cognitive burden upon the analyst, impede their ability to communicate critical information, which ultimately increases time duration of their forces OODA loop. Polysentry, a provider of software solutions for both defense and commercial customers, gives decision makers the ability to quickly identify correlations and patterns in unstructured intelligence reports and data records that would have otherwise gone unidentified. With minor adaptations to Polysentrys commercial technology, Air Force end-users will gain the ability to discover and extract quantifiable insights from large volumes of complex qualitative data, and then rapidly analyze those outputs to identify previously unseen strategic insights. Further testing and evaluation of our technology in a controlled cloud environment, using mission-relevant data, will continue to advance this capability.
