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Author, Editor

Author(s):

Parreira, Josiane Xavier
Donato, Debora
Michel, Sebastian
Weikum, Gerhard

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Not MPG Author(s):

Donato, Debora

Editor(s):

Dayal, Umeshwar
Whang, Kyu-Young
Lomet, David B.
Alonso, Gustavo
Lohman, Guy M.
Kersten, Martin L.
Cha, Sang Kyun
Kim, Young-Kuk

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Not MPII Editor(s):

Dayal, Umeshwar
Whang, Kyu-Young
Lomet, David B.
Alonso, Gustavo
Lohman, Guy M.
Kersten, Martin L.
Cha, Sang Kyun
Kim, Young-Kuk

BibTeX cite key*:

ParreiraDMW06

Title, Booktitle

Title*:

Efficient and Decentralized PageRank Approximation in a Peer-to-Peer Web Search Network

Booktitle*:

Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB 2006)

Event, URLs

URL of the conference:

http://aitrc.kaist.ac.kr/~vldb06/

URL for downloading the paper:

http://www.vldb.org/conf/2006/p415-xavier%20parreira.pdf

Event Address*:

Seoul, Korea

Language:

English

Event Date*
(no longer used):


Organization:


Event Start Date:

12 September 2006

Event End Date:

15 September 2006

Publisher

Name*:

ACM

URL:


Address*:

New York, USA

Type:


Vol, No, Year, pp.

Series:


Volume:


Number:


Month:


Pages:

415-426

Year*:

2006

VG Wort Pages:

60

ISBN/ISSN:

1-59593-385-9

Sequence Number:


DOI:




Note, Abstract, ©

Note:

Acceptance ratio 1:7

(LaTeX) Abstract:

PageRank-style (PR) link analyses are a cornerstone of Web
search engines and Web mining, but they are computationally
expensive. Recently, various techniques have been proposed
for speeding up these analyses by distributing the link
graph among multiple sites. However, none of these advanced
methods is suitable for a fully decentralized PR computation
in a peer-to-peer (P2P) network with autonomous
peers, where each peer can independently crawl Web fragments
according to the user's thematic interests. In such
a setting the graph fragments that different peers have locally
available or know about may arbitrarily overlap among
peers, creating additional complexity for the PR computation.
This paper presents the JXP algorithm for dynamically
and collaboratively computing PR scores of Web pages that
are arbitrarily distributed in a P2P network. The algorithm
runs at every peer, and it works by combining locally computed
PR scores with random meetings among the peers in
the network. It is scalable as the number of peers on the
network grows, and experiments as well as theoretical arguments
show that JXP scores converge to the true PR scores
that one would obtain by a centralized computation.



Download
Access Level:

Internal

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Databases and Information Systems Group

Audience:

Expert

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{ParreiraDMW06,
AUTHOR = {Parreira, Josiane Xavier and Donato, Debora and Michel, Sebastian and Weikum, Gerhard},
EDITOR = {Dayal, Umeshwar and Whang, Kyu-Young and Lomet, David B. and Alonso, Gustavo and Lohman, Guy M. and Kersten, Martin L. and Cha, Sang Kyun and Kim, Young-Kuk},
TITLE = {Efficient and Decentralized PageRank Approximation in a Peer-to-Peer Web Search Network},
BOOKTITLE = {Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB 2006)},
PUBLISHER = {ACM},
YEAR = {2006},
PAGES = {415--426},
ADDRESS = {Seoul, Korea},
ISBN = {1-59593-385-9},
NOTE = {Acceptance ratio 1:7},
}


Entry last modified by Adriana Davidescu, 07/20/2007
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Editor(s)
Adriana Davidescu
Created
06/01/2006 01:56:46 PM
Revisions
10.
9.
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7.
6.
Editor(s)
Adriana Davidescu
Uwe Brahm
Ralf Schenkel
Christine Kiesel
Christine Kiesel
Edit Dates
20.07.2007 17:35:48
04/10/2007 06:40:32 PM
30.03.2007 16:22:33
12.02.2007 09:54:13
30.10.2006 10:48:50