  A new generation of observational science instruments is dramatically
increasing collected data volumes in a range of fields. These instruments
include the Square Kilometre Array (SKA), Large Synoptic Survey Telescope
(LSST), terrestrial sensor networks, and NASA satellites participating in
"decadal survey" missions. Their unprecedented coverage and sensitivity will
likely reveal wholly new categories of unexpected and transient events.
Commensal methods passively analyze these data streams, recognizing anomalous
events of scientific interest and reacting in real time. We report on a case
example: V-FASTR, an ongoing commensal experiment at the Very Long Baseline
Array (VLBA) that uses online adaptive pattern recognition to search for
anomalous fast radio transients. V-FASTR triages a millisecond-resolution
stream of data and promotes candidate anomalies for further offline analysis.
It tunes detection parameters in real time, injecting synthetic events to
continually retrain itself for optimum performance. This self-tuning approach
retains sensitivity to weak signals while adapting to changing instrument
configurations and noise conditions. The system has operated since July 2011,
making it the longest-running real time commensal radio transient experiment to
date.
