Extracting Knowledge from Massive Astronomical Data Sets

The exponential growth of astronomical data collected by both ground-based and spaceborne instruments has fostered the growth of astroinformatics: a new discipline lying at the intersection between astronomy, applied computer science, and information and computation technologies. At the very heart of astroinformatics is a complex set of methodologies usually called data mining (DM) or knowledge discovery in databases (KDD). In the astronomical domain, DM/KDD are still in a very early usage stage, even though new methods and tools are being continuously deployed to cope with the massive data sets (MDSs) that can only grow in the future. In this paper, we briefly outline some general problems encountered when applying DM/KDD methods to astrophysical problems and describe the DAME (Data Mining and Exploration) Web application. While specifically tailored to work on MDSs, DAME can be effectively applied also to smaller data sets. As an illustration, we describe two applications of DAME to two different problems: the identification of candidate GCs in external galaxies and the classification of active Galactic nuclei (AGN). We believe that tools and services of this nature will become increasingly necessary for data-intensive astronomy (and indeed all sciences) in the twenty-first century.