Sliding-window top- k queries on uncertain streams

Sliding-window top- k queries on uncertain streams Recently, due to the imprecise nature of the data generated from a variety of streaming applications, such as sensor networks, query processing on uncertain data streams has become an important problem. However, all the existing works on uncertain data streams study unbounded streams. In this paper, we take the first step towards the important and challenging problem of answering sliding-window queries on uncertain data streams, with a focus on one of the most important types of queries—top- k queries. It is nontrivial to find an efficient solution for answering sliding-window top- k queries on uncertain data streams, because challenges not only stem from the strict space and time requirements of processing both arriving and expiring tuples in high-speed streams, but also rise from the exponential blowup in the number of possible worlds induced by the uncertain data model. In this paper, we design a unified framework for processing sliding-window top- k queries on uncertain streams. We show that all the existing top- k definitions in the literature can be plugged into our framework, resulting in several succinct synopses that use space much smaller than the window size, while they are also highly efficient in terms of processing time. We also extend our framework to answering multiple top- k queries. In addition to the theoretical space and time bounds that we prove for these synopses, we present a thorough experimental report to verify their practical efficiency on both synthetic and real data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Sliding-window top- k queries on uncertain streams

Loading next page...
 
/lp/springer_journal/sliding-window-top-k-queries-on-uncertain-streams-tjf53wLfRl
Publisher
Springer Journals
Copyright
Copyright © 2010 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-009-0171-0
Publisher site
See Article on Publisher Site

Abstract

Recently, due to the imprecise nature of the data generated from a variety of streaming applications, such as sensor networks, query processing on uncertain data streams has become an important problem. However, all the existing works on uncertain data streams study unbounded streams. In this paper, we take the first step towards the important and challenging problem of answering sliding-window queries on uncertain data streams, with a focus on one of the most important types of queries—top- k queries. It is nontrivial to find an efficient solution for answering sliding-window top- k queries on uncertain data streams, because challenges not only stem from the strict space and time requirements of processing both arriving and expiring tuples in high-speed streams, but also rise from the exponential blowup in the number of possible worlds induced by the uncertain data model. In this paper, we design a unified framework for processing sliding-window top- k queries on uncertain streams. We show that all the existing top- k definitions in the literature can be plugged into our framework, resulting in several succinct synopses that use space much smaller than the window size, while they are also highly efficient in terms of processing time. We also extend our framework to answering multiple top- k queries. In addition to the theoretical space and time bounds that we prove for these synopses, we present a thorough experimental report to verify their practical efficiency on both synthetic and real data.

Journal

The VLDB JournalSpringer Journals

Published: Jun 1, 2010

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