  The nature of scientific and technological data collection is evolving
rapidly: data volumes and rates grow exponentially, with increasing complexity
and information content, and there has been a transition from static data sets
to data streams that must be analyzed in real time. Interesting or anomalous
phenomena must be quickly characterized and followed up with additional
measurements via optimal deployment of limited assets. Modern astronomy
presents a variety of such phenomena in the form of transient events in digital
synoptic sky surveys, including cosmic explosions (supernovae, gamma ray
bursts), relativistic phenomena (black hole formation, jets), potentially
hazardous asteroids, etc. We have been developing a set of machine learning
tools to detect, classify and plan a response to transient events for astronomy
applications, using the Catalina Real-time Transient Survey (CRTS) as a
scientific and methodological testbed. The ability to respond rapidly to the
potentially most interesting events is a key bottleneck that limits the
scientific returns from the current and anticipated synoptic sky surveys.
Similar challenge arise in other contexts, from environmental monitoring using
sensor networks to autonomous spacecraft systems. Given the exponential growth
of data rates, and the time-critical response, we need a fully automated and
robust approach. We describe the results obtained to date, and the possible
future developments.

Instrumentation and Methods for Astrophysics