  Astronomy has been at the forefront of the development of the techniques and
methodologies of data intensive science for over a decade with large sky
surveys and distributed efforts such as the Virtual Observatory. However, it
faces a new data deluge with the next generation of synoptic sky surveys which
are opening up the time domain for discovery and exploration. This brings both
new scientific opportunities and fresh challenges, in terms of data rates from
robotic telescopes and exponential complexity in linked data, but also for data
mining algorithms used in classification and decision making. In this paper, we
describe how an informatics-based approach-part of the so-called "fourth
paradigm" of scientific discovery-is emerging to deal with these. We review our
experiences with the Palomar-Quest and Catalina Real-Time Transient Sky
Surveys; in particular, addressing the issue of the heterogeneity of data
associated with transient astronomical events (and other sensor networks) and
how to manage and analyze it.
