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Self-tuning Histograms: Building Histograms Without Looking at Data
 

Summary: Self-tuning Histograms: Building Histograms Without
Looking at Data
Ashraf Aboulnaga
Computer Sciences Department
University of Wisconsin - Madison
ashraf@cs.wisc.edu
Surajit Chaudhuri
Microsoft Research
surajitc@microsoft.com
Abstract
In this paper, we introduce self-tuning histograms. Although
similar in structure to traditional histograms, these histograms
infer data distributions not by examining the data or a sample
thereof, but by using feedback from the query execution engine
about the actual selectivity of range selection operators to
progressively refine the histogram. Since the cost of building and
maintaining self-tuning histograms is independent of the data size,
self-tuning histograms provide a remarkably inexpensive way to
construct histograms for large data sets with little up-front costs.
Self-tuning histograms are particularly attractive as an alternative

  

Source: Aboulnaga, Ashraf - School of Computer Science, University of Waterloo

 

Collections: Computer Technologies and Information Sciences