Cost-driven vertical class partitioning for methods in object oriented databases

Cost-driven vertical class partitioning for methods in object oriented databases In object-oriented databases (OODBs), a method encapsulated in a class typically accesses a few, but not all the instance variables defined in the class. It may thus be preferable to vertically partition the class for reducing irrelevant data (instance variables) accessed by the methods. Our prior work has shown that vertical class partitioning can result in a substantial decrease in the total number of disk accesses incurred for executing a set of applications, but coming up with an optimal vertical class partitioning scheme is a hard problem. In this paper, we present two algorithms for deriving optimal and near-optimal vertical class partitioning schemes. The cost-driven algorithm provides the optimal vertical class partitioning schemes by enumerating, exhaustively, all the schemes and calculating the number of disk accesses required to execute a given set of applications. For this, a cost model for executing a set of methods in an OODB system is developed. Since exhaustive enumeration is costly and only works for classes with a small number of instance variables, a hill-climbing heuristic algorithm (HCHA) is developed, which takes the solution provided by the affinity-based algorithm and improves it, thereby further reducing the total number of disk accesses incurred. We show that the HCHA algorithm provides a reasonable near-optimal vertical class partitioning scheme for executing a given set of applications. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Cost-driven vertical class partitioning for methods in object oriented databases

Loading next page...
 
/lp/springer_journal/cost-driven-vertical-class-partitioning-for-methods-in-object-oriented-qIKfrvUH1i
Publisher
Springer-Verlag
Copyright
Copyright © 2003 by Springer-Verlag
Subject
ComputerScience
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-002-0084-7
Publisher site
See Article on Publisher Site

Abstract

In object-oriented databases (OODBs), a method encapsulated in a class typically accesses a few, but not all the instance variables defined in the class. It may thus be preferable to vertically partition the class for reducing irrelevant data (instance variables) accessed by the methods. Our prior work has shown that vertical class partitioning can result in a substantial decrease in the total number of disk accesses incurred for executing a set of applications, but coming up with an optimal vertical class partitioning scheme is a hard problem. In this paper, we present two algorithms for deriving optimal and near-optimal vertical class partitioning schemes. The cost-driven algorithm provides the optimal vertical class partitioning schemes by enumerating, exhaustively, all the schemes and calculating the number of disk accesses required to execute a given set of applications. For this, a cost model for executing a set of methods in an OODB system is developed. Since exhaustive enumeration is costly and only works for classes with a small number of instance variables, a hill-climbing heuristic algorithm (HCHA) is developed, which takes the solution provided by the affinity-based algorithm and improves it, thereby further reducing the total number of disk accesses incurred. We show that the HCHA algorithm provides a reasonable near-optimal vertical class partitioning scheme for executing a given set of applications.

Journal

The VLDB JournalSpringer Journals

Published: Oct 1, 2003

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