EMBERS is a large-scale big data analytics system designed to use publically available data to predict population-level societal events such as civil unrest or disease outbreaks.

The usefulness of this predictive artificial intelligence system over the past 2 years is reviewed in an article in Big Data, the highly innovative, peer-reviewed journal from Mary Ann Liebert, Inc., publishers. The article is available free on the Big Data website.

In the article "Forecasting Significant Societal Events Using the EMBERS Streaming Predictive Analytics System," Andy Doyle and coauthors, CACI, Inc. (Lanham, MD), Virginia Tech (Arlington, VA), and BASIS Technology (Herndon, VA), describe the structure and function of the Early Model Based Event Recognition using Surrogates (EMBERS) system.

They describe EMBERS as a working example of a big data streaming architecture that processes large volumes of social media data and uses a variety of modeling approaches to make predictions.

"EMBERS represents a significant advance in our ability to make sense of large amounts of unstructured data in an automated manner," says Big Data Editor-in-Chief Vasant Dhar, Co-Director, Center for Business Analytics, Stern School of Business, New York University.

"The authors present an architecture that provides a scalable method for dealing with large streams of social media data emanating from Twitter. Although the focus of the paper is on predicting social unrest globally, the methods should be usable for processing these type of data for a variety of applications."