Effective Outlier Detection in Science Data Streams

Date Added: May 2010
Format: PDF

The growth in data volumes from all aspects of space and earth science (satellites, sensors, observatory monitoring systems, and simulations) requires more effective knowledge discovery and extraction algorithms. Among these are algorithms for outlier (novelty / surprise / anomaly) detection and discovery. Effective outlier detection in data streams is essential for rapid discovery of potentially interesting and/or hazardous events. Emerging unexpected conditions in hardware, software, or network resources need to be detected, characterized, and analyzed as soon as possible for obvious system health and safety reasons, just as emerging behaviors and variations in scientific targets should be similarly detected and characterized promptly in order to enable rapid decision support in response to such events.