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Doyle A, Katz G, Summers K, et al. Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System. Big Data. 2014;2(4):185-195doi: 10.1089/big.2014.0046.
Doyle, A., Katz, G., Summers, K., Ackermann, C., Zavorin, I., Lim, Z., Muthiah, S., Butler, P., Self, N., Zhao, L., Lu, C. T., Khandpur, R. P., Fayed, Y., & Ramakrishnan, N. (2014). Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System. Big data, 2(4), 185-195. https://doi.org/10.1089/big.2014.0046
Doyle, Andy, et al. "Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System." Big data vol. 2,4 (2014): 185-195. doi: https://doi.org/10.1089/big.2014.0046
Doyle A, Katz G, Summers K, Ackermann C, Zavorin I, Lim Z, Muthiah S, Butler P, Self N, Zhao L, Lu CT, Khandpur RP, Fayed Y, Ramakrishnan N. Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System. Big Data. 2014 Dec 01;2(4):185-195. doi: 10.1089/big.2014.0046. PMID: 25553271; PMCID: PMC4276118.
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Big Data. 2014 Dec 01;2(4):185-195. doi: 10.1089/big.2014.0046.

Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System.

Big data

Andy Doyle, Graham Katz, Kristen Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Patrick Butler, Nathan Self, Liang Zhao, Chang-Tien Lu, Rupinder Paul Khandpur, Youssef Fayed, Naren Ramakrishnan

Affiliations

  1. CACI Inc. , Lanham, Maryland.
  2. Virginia Tech , Arlington, Virginia.
  3. BASIS Technology , Herndon, Virginia.

PMID: 25553271 PMCID: PMC4276118 DOI: 10.1089/big.2014.0046

Abstract

Developed under the Intelligence Advanced Research Project Activity Open Source Indicators program, Early Model Based Event Recognition using Surrogates (EMBERS) is a large-scale big data analytics system for forecasting significant societal events, such as civil unrest events on the basis of continuous, automated analysis of large volumes of publicly available data. It has been operational since November 2012 and delivers approximately 50 predictions each day for countries of Latin America. EMBERS is built on a streaming, scalable, loosely coupled, shared-nothing architecture using ZeroMQ as its messaging backbone and JSON as its wire data format. It is deployed on Amazon Web Services using an entirely automated deployment process. We describe the architecture of the system, some of the design tradeoffs encountered during development, and specifics of the machine learning models underlying EMBERS. We also present a detailed prospective evaluation of EMBERS in forecasting significant societal events in the past 2 years.

References

  1. Behav Res Methods. 2007 Aug;39(3):600-5 - PubMed
  2. J Am Med Inform Assoc. 2008 Mar-Apr;15(2):150-7 - PubMed
  3. PLoS Med. 2008 Jul 8;5(7):e151 - PubMed

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