東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back to Search results for [ null ]

Switch To: Labeled | MARC Mode | ISBD

Structural mining of large-scale beh...

Meiss, Mark.

Linked to FindBook

Google Book

Amazon

博客來

Structural mining of large-scale behavioral data from the Internet.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Structural mining of large-scale behavioral data from the Internet./
Author:	Meiss, Mark.
Description:	321 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 4352.
Contained By:	Dissertation Abstracts International71-07B.
Subject:	Web Studies. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3409772
ISBN:	9781124075013

Structural mining of large-scale behavioral data from the Internet.
Meiss, Mark.

Structural mining of large-scale behavioral data from the Internet. - 321 p.

Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 4352.

Thesis (Ph.D.)--Indiana University, 2010.

As the Internet becomes ever more pervasive in the lives of hundreds of millions of people, our understanding of its physical structure has outpaced our understanding of the dynamic patterns of traffic generated by its users. This work aims to develop a better understanding of the structure of Internet traffic in a manner consistent with individual privacy and computational constraints. I first examine network flow data from the Internet2 network, using it to form "behavioral networks" based on the flows attributable to specific network applications. The heavy-tailed distributions associated with these networks suggest unbounded variance and poorly defined means in distributions of user behavior. However, a novel application of hierarchical clustering to similarity data derived from these networks makes it possible to classify network applications robustly based on their observed behavior. I then focus on Web traffic, using a large collection of HTTP request data to build a weighted subset of the Web graph. Analysis of this weighted graph reveals more heavy-tailed distributions and the presence of a large body of stationary traffic. The traffic data are also shown to contradict key assumptions of the random surfer model used by PageRank. I conclude with the development of ABC, an behaviorally plausible agent-based model of Web traffic that incorporates backtracking, bookmarks, and a sense of topical locality. The ABC model is shown to approximate real user activity more accurately than PageRank on both artificial and empirically generated graphs.

ISBN: 9781124075013Subjects--Topical Terms:

1026830
Web Studies.

Structural mining of large-scale behavioral data from the Internet.
LDR:02720nam 2200349 4500 001 1401600
005 20111017084402.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124075013
035 $a (UMI)AAI3409772
035 $a AAI3409772
040 $a UMI $c UMI
100 1 $a Meiss, Mark. $3 1680746
245 1 0 $a Structural mining of large-scale behavioral data from the Internet.
300 $a 321 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 4352.
500 $a Adviser: Filippo Menczer.
502 $a Thesis (Ph.D.)--Indiana University, 2010.
520 $a As the Internet becomes ever more pervasive in the lives of hundreds of millions of people, our understanding of its physical structure has outpaced our understanding of the dynamic patterns of traffic generated by its users. This work aims to develop a better understanding of the structure of Internet traffic in a manner consistent with individual privacy and computational constraints. I first examine network flow data from the Internet2 network, using it to form "behavioral networks" based on the flows attributable to specific network applications. The heavy-tailed distributions associated with these networks suggest unbounded variance and poorly defined means in distributions of user behavior. However, a novel application of hierarchical clustering to similarity data derived from these networks makes it possible to classify network applications robustly based on their observed behavior. I then focus on Web traffic, using a large collection of HTTP request data to build a weighted subset of the Web graph. Analysis of this weighted graph reveals more heavy-tailed distributions and the presence of a large body of stationary traffic. The traffic data are also shown to contradict key assumptions of the random surfer model used by PageRank. I conclude with the development of ABC, an behaviorally plausible agent-based model of Web traffic that incorporates backtracking, bookmarks, and a sense of topical locality. The ABC model is shown to approximate real user activity more accurately than PageRank on both artificial and empirically generated graphs.
590 $a School code: 0093.
650 4 $a Web Studies. $3 1026830
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Computer Science. $3 626642
690 $a 0646
690 $a 0800
690 $a 0984
710 2 $a Indiana University. $b Informatics. $3 1680747
773 0 $t Dissertation Abstracts International $g 71-07B.
790 1 0 $a Menczer, Filippo, $e advisor
790 1 0 $a Menczer, Filippo $e committee member
790 1 0 $a Vespignani, Alessandro $e committee member
790 1 0 $a Boerner, Katy $e committee member
790 1 0 $a Gupta, Minaxi $e committee member
790 1 0 $a Connelly, Kay $e committee member
790 $a 0093
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3409772