Defense Advanced Research Projects AgencyTagged Content List

Analytics for Data at Massive Scales

Extracting information from large data sets

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Training, which is conducted in classroom, field, and virtual settings, is a critical element of military readiness. Large-scale social networks, interactive content, and ubiquitous mobile access are emerging as driving technologies in education and training. At the same time, education analytics presents new opportunities for assessing the effectiveness of training strategies, understanding trends and effects in large volumes of education data, and relating these back to alternative modes of instruction.
Malicious actors in cyberspace currently operate with little fear of being caught due to the fact that it is extremely difficult, in some cases perhaps even impossible, to reliably and confidently attribute actions in cyberspace to individuals. The reason cyber attribution is difficult stems at least in part from a lack of end-to-end accountability in the current Internet infrastructure.
The threat of distributed denial of service (DDoS) attacks has been well-recognized in the data networking world for two decades. Such attacks are orchestrated by sets of networked hosts that collectively act to disrupt or deny access to information, communications or computing capabilities, generally by exhausting critical resources such as bandwidth, processor capacity or memory of targeted resources.
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.