# Multi-objective parametric query optimization

Multi-objective parametric query optimization Classical query optimization compares query plans according to one cost metric and associates each plan with a constant cost value. In this paper, we introduce the multi-objective parametric query optimization (MPQO) problem where query plans are compared according to multiple cost metrics and the cost of a given plan according to a given metric is modeled as a function that depends on multiple parameters. The cost metrics may, for instance, include execution time or monetary fees; a parameter may represent the selectivity of a query predicate that is unspecified at optimization time. MPQO generalizes parametric query optimization (which allows multiple parameters but only one cost metric) and multi-objective query optimization (which allows multiple cost metrics but no parameters). We formally analyze the novel MPQO problem and show why existing algorithms are inapplicable. We present a generic algorithm for MPQO and a specialized version for MPQO with piecewise-linear plan cost functions. We prove that both algorithms find all relevant query plans and experimentally evaluate the performance of our second algorithm in multiple scenarios. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

# Multi-objective parametric query optimization

, Volume 26 (1) – Aug 18, 2016
18 pages

/lp/springer_journal/multi-objective-parametric-query-optimization-4do1G7j0DE
Publisher
Springer Journals
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-016-0439-0
Publisher site
See Article on Publisher Site

### Abstract

Classical query optimization compares query plans according to one cost metric and associates each plan with a constant cost value. In this paper, we introduce the multi-objective parametric query optimization (MPQO) problem where query plans are compared according to multiple cost metrics and the cost of a given plan according to a given metric is modeled as a function that depends on multiple parameters. The cost metrics may, for instance, include execution time or monetary fees; a parameter may represent the selectivity of a query predicate that is unspecified at optimization time. MPQO generalizes parametric query optimization (which allows multiple parameters but only one cost metric) and multi-objective query optimization (which allows multiple cost metrics but no parameters). We formally analyze the novel MPQO problem and show why existing algorithms are inapplicable. We present a generic algorithm for MPQO and a specialized version for MPQO with piecewise-linear plan cost functions. We prove that both algorithms find all relevant query plans and experimentally evaluate the performance of our second algorithm in multiple scenarios.

### Journal

The VLDB JournalSpringer Journals

Published: Aug 18, 2016

## 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
that matters to you.

over 18 million articles from more than
15,000 peer-reviewed journals.

All for just \$49/month

### 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.

DeepDyve

DeepDyve

### Pro

Price

FREE

\$49/month
\$360/year

Save searches from
PubMed

Create lists to

Export lists, citations