Title :
The EMBERS architecture for streaming predictive analytics
Author :
Doyle, Andy ; Katz, Gil ; Summers, Kathryn ; Ackermann, Chris ; Zavorin, Ilya ; Zunsik Lim ; Muthiah, Sathappan ; Liang Zhao ; Chang-Tien Lu ; Butler, Patrick ; Khandpur, Rupinder Paul ; Fayed, Youssef ; Ramakrishnan, N.
Author_Institution :
CACI Inc., Lanham, MD, USA
Abstract :
Developed under the IARPA Open Source Initiative program, EMBERS (Early Model Based Event Recognition using Surrogates) is a large-scale Big-Data analytics system for forecasting significant societal events, such as civil unrest incidents and disease outbreaks on the basis of continuous, automated analysis of large volumes of publicly available data. It has been operational since November of 2012, delivering approximately 50 predictions each day. EMBERS is built on a streaming, scalable, share-nothing architecture and is deployed on Amazon Web Services (AWS).
Keywords :
Big Data; Web services; cloud computing; public domain software; software architecture; AWS; Amazon Web Services; EMBERS architecture; IARPA open source initiative program; civil unrest incidents; continuous automated analysis; disease outbreaks; early model-based event recognition-using-surrogates; large-scale Big-Data analytics system; predictive analytics streaming; publicly available data; societal event forecasting; streaming-scalable-share-nothing architecture; Big data; Computer architecture; Data models; Data visualization; Diseases; Feeds; Predictive models;
Conference_Titel :
Big Data (Big Data), 2014 IEEE International Conference on
Conference_Location :
Washington, DC
DOI :
10.1109/BigData.2014.7004477
