Defense Advanced Research Projects AgencyTagged Content List

Data Analysis at Massive Scales

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

Showing 6 results for Data + Systems RSS
Computing performance has steadily increased against the trajectory set by Moore’s Law, and networking performance has accelerated at a similar rate. Despite these connected evolutions in network and server technology however, the network stack, starting with the network interface card (NIC) – or the hardware that bridges the network/server boundary – has not kept pace. Today, network interface hardware is hampering data ingest from the network to processing hardware. Additional factors, such as limitations in server memory technologies, memory copying, poor application design, and competition for shared resources, has resulted in network subsystems that are creating a bottleneck within the network stack and are throttling application throughput.
May 1, 2018,
CENTRA Technology Incorporated Conference Center
DARPA’s Tactical Technology Office is hosting a Proposers Day to provide information to potential applicants on the structure and objectives of the new Urban Reconnaissance through Supervised Autonomy (URSA) program. URSA aims to develop technology to enable autonomous systems operated and supervised by U.S. ground forces to detect hostile forces and establish positive identification of combatants before U.S. troops encounter them. The URSA program seeks to overcome the inherent complexity of the urban environment by combining new knowledge about human behaviors, autonomy algorithms, integrated sensors, multiple sensor modalities, and measurable human responses to discriminate the subtle differences between hostile individuals and noncombatants.
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.
The Understanding Group Biases (UGB) program seeks to develop and prove out capabilities that can radically enhance the scale, speed, and scope of automated, ethnographic-like methods for capturing group biases and cultural models from increasingly available large digital datasets.
Contact DSO Program Managers to discuss your ideas.