Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches describes basic principles and recent developments in building approximate synopses (i.e., lossy, compressed representations) of massive data. Such synopses enable approximate query processing, in which the user's query is executed against the synopsis instead of the original data. The monograph focuses on the four main families of synopses: random samples, histograms, wavelets, and sketches. A random sample comprises a "representative" subset of the data values of interest, obtained via a stochastic mechanism. Samples can be...