Defense Advanced Research Projects AgencyTagged Content List

Analytics for Data at Massive Scales

Extracting information from large data sets

Showing 6 results for Analytics + ISR RSS
09/05/2013
Military operations depend upon the unimpeded flow of accurate and relevant information to support timely decisions related to battle planning and execution. To address these needs, numerous intelligence systems and technologies have been developed over the past 20 years, but each of these typically provides only a partial picture of the battlefield, and integrating the information has proven to be burdensome and inefficient.
10/11/2017
The rapid pace of new commercial satellite constellation launches has led to a significant increase in the amount and availability of geospatial imagery. Unfortunately, no straightforward way currently exists for analysts to access and analyze all of that imagery. The current ad hoc, time-intensive approach requires gathering and curating data from a large number of available sources, downloading it to specific locations, and running it through separate suites of analytics tools.
Military intelligence analysts face the monumental and escalating task of analyzing massive volumes of complex data from multiple, diverse sources such as physical sensors, human contacts and contextual databases. These analysts consume and process information from all available sources to provide mission-relevant, timely insights to commanders. To enhance this largely manual process, analysts require more effective and efficient means to receive, correlate, analyze, report and share intelligence.
Program Manager
Dr. David Doermann joined DARPA in April 2014. His areas of technical interest span language and media processing and exploitation, vision and mobile technologies. He comes to DARPA with a vision of increasing capabilities through joint vision/language interaction for triage and forensics applications.
Program Manager
Mr. Steve Jameson joined DARPA in August 2014. His current research focuses on technologies to enable situation understanding, improve effectiveness and timeliness of decision-making, and build trust between humans and autonomous reasoning systems. Specific interests include knowledge representation, techniques for causal modeling, reasoning, and inference, as well as technologies to support mixed initiative reasoning, with a focus on enabling non-expert users to effectively interact with automated reasoning systems.