Defense Advanced Research Projects AgencyTagged Content List

Analytics for Data at Massive Scales

Extracting information from large data sets

Showing 26 results for Analytics + Programs RSS
The United States Government has an interest in developing and maintaining a strategic understanding of events, situations, and trends around the world, in a variety of domains. The information used in developing this understanding comes from many disparate sources, in a variety of genres, and data types, and as a mixture of structured and unstructured data. Unstructured data can include text or speech in English and a variety of other languages, as well as images, videos, and other sensor information.
The Anomaly Detection at Multiple Scales (ADAMS) program creates, adapts and applies technology to anomaly characterization and detection in massive data sets. Anomalies in data cue the collection of additional, actionable information in a wide variety of real world contexts. The initial application domain is insider threat detection in which malevolent (or possibly inadvertent) actions by a trusted individual are detected against a background of everyday network activity.
Expanded global access to diverse means of communication is resulting in more information being produced in more languages more quickly than ever before. The volume of information encountered by DoD, the speed at which it arrives, and the diversity of languages and media through which it is communicated make identifying and acting on relevant information a serious challenge. At the same time, there is a need to communicate with non-English-speaking local populations of foreign countries, but it is at present costly and difficult for DoD to do so.
Over the last 15 years, the U.S. military has increasingly been called upon to face complex operational environments (OE) and diverse enemies. Such complex OEs require the actions of U.S. forces and host-nation or coalition partners to be based on a common holistic understanding of the OE (e.g., governments, population groups, security forces, and violent non-state actors) and, in particular, the causal dynamics that can manifest as unanticipated and often counter-intuitive outcomes.
Understanding the complex and increasingly data-intensive world around us relies on the construction of robust empirical models, i.e., representations of real, complex systems that enable decision makers to predict behaviors and answer “what-if” questions. Today, construction of complex empirical models is largely a manual process requiring a team of subject matter experts and data scientists.