Synopses for massive data: samples, histograms, wavelets, sketchesSynopses for massive data: samples, histograms, wavelets, sketches Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-302012enMethods for Approximate Query Processing (AQP) are essential for dealing with massive data. They are often the only means of providing interactive response times when exploring massive datasets, and are also needed to handle high speed data streams. These methods proceed by computing a lossy, compact synopsis of the data, and then executing the query of interest against the synopsis rather than the entire dataset. We describe basic principles and recent developments in AQP. We focus on four key synopses: random samples, histograms, wavelets, and sketches. We consider issues such as accuracy, space and time effi- ciency, optimality, practicality, range of applicability, error bounds on query answers, and incremental maintenance. We also discuss the tradeoffs between the different synopsis types.http://creativecommons.org/licenses/by/4.0/Foundations and Trends in Databases41-31-294 Cormode, Graham, 1977- Garofalakis Minos Γαροφαλακης Μινως Haas Peter J. Jermaine Chris Now Publishers Histograms Approximate query processing