Dynamic Data-Driven Modeling, Sampling and Monitoring of Big Spatial-Temporal Data Streams for Real-Time Solar Flare Detection
Abstract
Big Data Streams that consist of massive arrays of real-time, continuously and sequentiallyordered observations have become widely available in many DoD applications. This provideunprecedented opportunity to gain system-wide situational awareness through real-time anomalydetection and fault localization. However, the existing literature is still lacking efficient onlinemonitoring schemes that are tailored to the characteristics of Big Data Streams.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 09, 2018
- Source ID
- FA95501810145
Entities
People
- Kaibo Liu
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Wisconsin System