Toward efficient multidimensional subspace skyline computation

Toward efficient multidimensional subspace skyline computation Skyline queries have attracted considerable attention to assist multicriteria analysis of large-scale datasets. In this paper, we focus on multidimensional subspace skyline computation that has been actively studied for two approaches. First, to narrow down a full-space skyline, users may consider multiple subspace skylines reflecting their interest. For this purpose, we tackle the concept of a skycube, which consists of all possible non-empty subspace skylines in a given full space. Second, to understand diverse semantics of subspace skylines, we address skyline groups in which a skyline point (or a set of skyline points) is annotated with decisive subspaces. Our primary contributions are to identify common building blocks of the two approaches and to develop orthogonal optimization principles that benefit both approaches. Our experimental results show the efficiency of proposed algorithms by comparing them with state-of-the-art algorithms in both synthetic and real-life datasets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Toward efficient multidimensional subspace skyline computation

Loading next page...
 
/lp/springer_journal/toward-efficient-multidimensional-subspace-skyline-computation-wvbBhb53FN
Publisher
Springer Journals
Copyright
Copyright © 2013 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-013-0317-y
Publisher site
See Article on Publisher Site

Abstract

Skyline queries have attracted considerable attention to assist multicriteria analysis of large-scale datasets. In this paper, we focus on multidimensional subspace skyline computation that has been actively studied for two approaches. First, to narrow down a full-space skyline, users may consider multiple subspace skylines reflecting their interest. For this purpose, we tackle the concept of a skycube, which consists of all possible non-empty subspace skylines in a given full space. Second, to understand diverse semantics of subspace skylines, we address skyline groups in which a skyline point (or a set of skyline points) is annotated with decisive subspaces. Our primary contributions are to identify common building blocks of the two approaches and to develop orthogonal optimization principles that benefit both approaches. Our experimental results show the efficiency of proposed algorithms by comparing them with state-of-the-art algorithms in both synthetic and real-life datasets.

Journal

The VLDB JournalSpringer Journals

Published: May 22, 2013

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