Intelligence analytics are capabilities that help analysts work with data in ways that make the meaning of the data more understandable and, in sophisticated ways, empower intelligence analysts to find clues and create knowledge that would be otherwise hidden within vast stores of sensor data and archived reporting. Analytics, most simply, are automated components that help analysts work with data.
Invisible to most users, ISS analytics perform four primary background functions that help analysts make sense of their data. The first function constitutes a workflow that begins with data parsing and formatting, which in turn prepares the data for concept extraction. Secondly, the concept extraction function creates new data that describes the original data by flagging instances of entities, events, locations, and time.
The third function is association, which creates basic context by linking together the instances of entities and events to their specific times and places. Finally, the fourth enrichment function is the correlation of associated events—the linkage of many related entities and events within the context of their specific time and places.
ISS’ analytic components also function at a level that directly impacts the users’ experience. Examples of these components include a user-controlled sandbox to facilitate finding and comparing existing intelligence and creation of intuitive visualizations; automatic source material meta-tagging for classification, sources, and authority; user-customizable production and coordination workflows with organizational templates; and support for transorganizational/transfunctional collaboration.
Geoprocessing in WebTAS Enterprise – cost distance calculation, viewshed analysis (determining whether a location is visible from a specified second location), and density analysis.
Association Discovery Framework