  The changing heavens have played a central role in the scientific effort of
astronomers for centuries. Galileo's synoptic observations of the moons of
Jupiter and the phases of Venus starting in 1610, provided strong refutation of
Ptolemaic cosmology. In more modern times, the discovery of a relationship
between period and luminosity in some pulsational variable stars led to the
inference of the size of the Milky Way, the distance scale to the nearest
galaxies, and the expansion of the Universe. Distant explosions of supernovae
were used to uncover the existence of dark energy and provide a precise
numerical account of dark matter. Indeed, time-domain observations of transient
events and variable stars, as a technique, influences a broad diversity of
pursuits in the entire astronomy endeavor. While, at a fundamental level, the
nature of the scientific pursuit remains unchanged, the advent of astronomy as
a data-driven discipline presents fundamental challenges to the way in which
the scientific process must now be conducted. Digital images (and data cubes)
are not only getting larger, there are more of them. On logistical grounds,
this taxes storage and transport systems. But it also implies that the intimate
connection that astronomers have always enjoyed with their data---from
collection to processing to analysis to inference---necessarily must evolve.
The pathway to scientific inference is now influenced (if not driven by) modern
automation processes, computing, data-mining and machine learning. The emerging
reliance on computation and machine learning is a general one, but the
time-domain aspect of the data and the objects of interest presents some unique
challenges, which we describe and explore in this chapter.
