  An automated, rapid classification of transient events detected in the modern
synoptic sky surveys is essential for their scientific utility and effective
follow-up using scarce resources. This presents some unusual challenges: the
data are sparse, heterogeneous and incomplete; evolving in time; and most of
the relevant information comes not from the data stream itself, but from a
variety of archival data and contextual information (spatial, temporal, and
multi-wavelength). We are exploring a variety of novel techniques, mostly
Bayesian, to respond to these challenges, using the ongoing CRTS sky survey as
a testbed. The current surveys are already overwhelming our ability to
effectively follow all of the potentially interesting events, and these
challenges will grow by orders of magnitude over the next decade as the more
ambitious sky surveys get under way. While we focus on an application in a
specific domain (astrophysics), these challenges are more broadly relevant for
event or anomaly detection and knowledge discovery in massive data streams.
