Defense Advanced Research Projects AgencyTagged Content List

Data Analysis at Massive Scales

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

Showing 32 results for Data + News RSS
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 tens of thousands of miles per hour. The scales and speeds in this extreme environment are difficult enough to grasp conceptually, let alone operationally, as is required for commanders overseeing the nation’s increasingly critical space assets.
More than 500,000 pieces of manmade space debris—including spent rocket stages, defunct satellites, and fragments as small as flecks of paint—currently hurtle around the Earth at roughly 17,000 miles per hour. At those speeds, impacts involving even the smallest of those items can damage satellites and spawn chain reactions of collisions, increasing the amount of orbital flotsam and creating “minefields” in space that can remain unpassable for centuries.
As the complexity and volume of global digital data grows, so too does the need for more capable and compact means of processing and storing data. To address this challenge, DARPA has announced its Molecular Informatics program, which seeks a new paradigm for data storage, retrieval, and processing. Instead of relying on the binary digital logic of computers based on the Von Neumann architecture, Molecular Informatics aims to investigate and exploit the wide range of structural characteristics and properties of molecules to encode and manipulate data.
The U.S. government has always had an interest in developing and maintaining a strategic understanding of events, situations, and trends around the world. In recent years, however, information complexity has exceeded the capacity of analysts to glean meaningful or actionable insights as data pours in from disparate sources, across a variety of genres, and a mixture of structured and unstructured forms, from military intelligence to social media to accurate and inaccurate news.
Advances in artificial intelligence (AI) are making virtual and robotic assistants increasingly capable in performing complex tasks. For these “smart” machines to be considered safe and trustworthy collaborators with human partners, however, robots must be able to quickly assess a given situation and apply human social norms. Such norms are intuitively obvious to most people—for example, the result of growing up in a society where subtle or not-so-subtle cues are provided from childhood about how to appropriately behave in a group setting or respond to interpersonal situations. But teaching those rules to robots is a novel challenge.