Defense Advanced Research Projects AgencyTagged Content List

Analytics for Data at Massive Scales

Extracting information from large data sets

Showing 53 results for Analytics RSS
In every population that encounters an infectious organism, a few individuals prove to be resilient—unfazed by that pathogen because they are either resistant to it (their immune systems keep the pathogen from multiplying to dangerous levels) or tolerant (they don’t get as sick as they otherwise might despite carrying high pathogen loads). Conventional disease treatments such as antibiotics have almost exclusively sought to emulate natural resistance by keeping patients’ pathogen levels as low as possible.
The chikungunya virus (CHIKV) is quickly spreading through the Western Hemisphere; as of May 15, 2015, the Pan-American Health Organization (PAHO) had tallied close to 1.4 million suspected cases and more than 33,000 confirmed cases since the virus’ first appearance in the Americas in December 2013. Spread by mosquitoes, chikungunya is rarely fatal but can cause debilitating joint and muscle pain, fever, nausea, fatigue and rash, and poses a growing public health and national security risk. Governments and health organizations could take more effective proactive steps to limit the spread of CHIKV if they had accurate forecasts of where and when it would appear. But such predictions for CHIKV and other emerging infectious diseases remain beyond the reach of current modeling capabilities.
Popular search engines are great at finding answers for point-of-fact questions like the elevation of Mount Everest or current movies running at local theaters. They are not, however, very good at answering what-if or predictive questions—questions that depend on multiple variables, such as “What influences the stock market?” or “What are the major drivers of environmental stability?” In many cases that shortcoming is not for lack of relevant data. Rather, what’s missing are empirical models of complex processes that influence the behavior and impact of those data elements.
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.
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.