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