Histograms based on the minimum description length principle

Histograms based on the minimum description length principle Histograms have been widely used for selectivity estimation in query optimization, as well as for fast approximate query answering in many OLAP, data mining, and data visualization applications. This paper presents a new family of histograms, the Hierarchical Model Fitting (HMF) histograms , based on the Minimum Description Length principle. Rather than having each bucket of a histogram described by the same type of model, the HMF histograms employ a local optimal model for each bucket. The improved effectiveness of the locally chosen models offsets more than the overhead of keeping track of the representation of each individual bucket. Through a set of experiments, we show that the HMF histograms are capable of providing more accurate approximations than previously proposed techniques for many real and synthetic data sets across a variety of query workloads. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Histograms based on the minimum description length principle

Loading next page...
 
/lp/springer_journal/histograms-based-on-the-minimum-description-length-principle-3auhvBUy37
Publisher
Springer-Verlag
Copyright
Copyright © 2008 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-006-0015-0
Publisher site
See Article on Publisher Site

Abstract

Histograms have been widely used for selectivity estimation in query optimization, as well as for fast approximate query answering in many OLAP, data mining, and data visualization applications. This paper presents a new family of histograms, the Hierarchical Model Fitting (HMF) histograms , based on the Minimum Description Length principle. Rather than having each bucket of a histogram described by the same type of model, the HMF histograms employ a local optimal model for each bucket. The improved effectiveness of the locally chosen models offsets more than the overhead of keeping track of the representation of each individual bucket. Through a set of experiments, we show that the HMF histograms are capable of providing more accurate approximations than previously proposed techniques for many real and synthetic data sets across a variety of query workloads.

Journal

The VLDB JournalSpringer Journals

Published: May 1, 2008

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off