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Web log analysis: Experimental studies

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Date Issued:
2005
Summary:
With rapid growth of the World Wide Web, web performance becomes increasingly important for modern businesses, especially for e-commerce. As we all know, web server logs contain potentially useful empirical data to improve web server performance. In this thesis, we discuss some topics related to the analysis of a website's server logs for enhancing server performance, which will benefit some applications in business. Markov chain models are used and allow us to dynamically model page sequences extracted from server logs. My experimental studies contain three major parts. First, I present a workload characterization study of the website used for my research. Second, Markov chain models are constructed for both page request and page-visiting sequence prediction. Finally, I carefully evaluate the constructed models using an independent test data set, which is from server logs on a different day. The research results demonstrate the effectiveness of Markov chain models for characterizing page-visiting sequences.
Title: Web log analysis: Experimental studies.
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Name(s): Yang, Zhijian.
Florida Atlantic University, Degree grantor
Zhong, Shi, Thesis advisor
Pandya, Abhijit S., Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 2005
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 80 p.
Language(s): English
Summary: With rapid growth of the World Wide Web, web performance becomes increasingly important for modern businesses, especially for e-commerce. As we all know, web server logs contain potentially useful empirical data to improve web server performance. In this thesis, we discuss some topics related to the analysis of a website's server logs for enhancing server performance, which will benefit some applications in business. Markov chain models are used and allow us to dynamically model page sequences extracted from server logs. My experimental studies contain three major parts. First, I present a workload characterization study of the website used for my research. Second, Markov chain models are constructed for both page request and page-visiting sequence prediction. Finally, I carefully evaluate the constructed models using an independent test data set, which is from server logs on a different day. The research results demonstrate the effectiveness of Markov chain models for characterizing page-visiting sequences.
Identifier: 9780496967308 (isbn), 13202 (digitool), FADT13202 (IID), fau:10060 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.)--Florida Atlantic University, 2005.
Subject(s): Markov processes
Operations research
Business enterprises--Computer networks
Electronic commerce--Data processing
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/13202
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Owner Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.