Defense Advanced Research Projects AgencyTagged Content List

Analytics for Data at Massive Scales

Extracting information from large data sets

Showing 70 results for Analytics RSS
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.
The social sciences can play important roles in assisting military planners and decision-makers who are trying to understand complex human social behaviors and systems, potentially facilitating a wide range of missions including humanitarian, stability, and counter-insurgency operations. Current social science approaches to studying behavior rely on a variety of modeling methods—both qualitative and quantitative—which seek to make inferences about the causes of social phenomena on the basis of observations in the real-world. Yet little is known about how accurate these methods and models really are, let alone whether the connections they observe and predict are truly matters of cause and effect or mere correlations.
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.
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.
The Department of Defense’s information technology (IT) infrastructure is made up of a large, complex network of connected local networks comprised of thousands of devices. Cyber defenders must understand and monitor the entire environment to defend it effectively. Toward this end, cyber-defenders work to correlate and understand the information contained in log files, executable files, databases of varying formats, directory structures, communication paths, file and message headers, as well as in the volatile and non-volatile memory of the devices on the network. Meanwhile, adversaries increasingly use targeted attacks that disguise attacks as legitimate actions, making discovery far more difficult. It is within this complicated web of networked systems that cyber defenders must find targeted cyber-attacks.