Defense Advanced Research Projects AgencyTagged Content List

Data Analysis at Massive Scales

Extracting information and insights from massive datasets; "big data"; "data mining"

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The Geospatial Cloud Analytics (GCA) program is developing technology to rapidly access the most up-to-date commercial and open-source satellite imagery, as well as automated machine learning tools to analyze this data. Current approaches to geospatial analysis are ad hoc and time intensive, as they require gathering and curating data from a large number of available sources, downloading the data to specific locations, and running it through separate suites of analytics tools.
Modern computing systems are incapable of creating sufficient security protections such that they can be trusted with the most sensitive data while simultaneously being exposed to untrusted data streams. In certain places, the Department of Defense (DoD) and commercial industry have adopted a series of air-gaps – or breaks between computing systems – to prevent the leakage and compromise of sensitive information.
What is opaque to outsiders is often obvious – even if implicit – to locals. Habitus aims to capture and make local knowledge available to military operators, providing them with an insider view to support decision making.
Military commanders responsible for situational awareness and command and control of assets in space know all too well the challenge that comes from the vast size of the space domain. The volume of Earth’s operational space domain is hundreds of thousands times larger than the Earth’s oceans. It contains thousands of objects hurtling at up to 17,000 miles per hour.
Social media, sensor feeds, and scientific studies generate large amounts of valuable data. However, understanding the relationships among this data can be challenging. Graph analytics has emerged as an approach by which analysts can efficiently examine the structure of the large networks produced from these data sources and draw conclusions from the observed patterns.