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.

Analytic Components

Techniques for processing data when the size of the data becomes too difficult to manage using traditional data management techniques; this may involve distributed computing techniques that facilitate the coordination of large numbers of computers, each working on some subset of the overall data set.
A broad category which derives high-quality information from text; text analytics may cover natural language processing, information extraction and named-entity recognition (person, organization, relationship, event, time, and location extraction), clustering algorithms, and classification (aka categorization).

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A wide variety of techniques related to analyzing geospatial data.

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Geoprocessing in WebTAS Enterprise – cost distance calculation, viewshed analysis (determining whether a location is visible from a specified second location), and density analysis.

Facilitating near real-time multi-INT fusion and predictive analysis.
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A set of services enabling relationship association discovery, anomaly detection, and signature discovery.
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Association Discovery Framework

Enables analysis of social relationships in terms of network theory consisting of links or connections.

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WebTAS Enterprise