We focus on four key synopses: random samples, histograms, wavelets, and sketches. We consider issues such as accuracy, space and time effi- ciency, optimality, ...
It focuses on the four main families of synopses: random samples, histograms, wavelets, and sketches. A random sample comprises a "representative" subset of the ...
We focus on four key synopses: random samples, histograms, wavelets, and sketches. ... synopses (i.e., lossy, compressed representations) of massive data.
Dec 31, 2011 · We describe basic principles and recent developments in AQP. We focus on four key synopses: random samples, histograms, wavelets, and sketches.
A histogram summarizes a dataset by grouping the data values into subsets, or “buckets,” and then, for each bucket, computing a small set of summary statistics ...
We describe basic principles and recent developments in AQP. We focus on four key synopses: random samples, histograms, wavelets, and sketches. We consider ...
Synopses for Massive Data: Samples, Histograms, Wavelets ...
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Book by Graham Cormode
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches describes basic principles and recent developments in building approximate synopses (that is, lossy, compressed representations) of massive data. ... Google Books
Originally published: December 30, 2011
Author: Graham Cormode
Dec 1, 2011 · Synopses for massive data: Samples, histograms, wavelets, sketches for Foundations and Trends in Databases by Graham Cormode et al.
Abstract: Describes basic principles and recent developments in building approximate synopses (that is, lossy, compressed representations) of massive data.
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches (Foundations and Trends(r) in Databases). Customer reviews. 5.0 out of 5 stars5 out of 5.