Using similarity measure to enhance the robustness of Web access prediction model

Ben Niu; Chi Keung Simon Shiu

Using similarity measure to enhance the robustness of Web access prediction model

Ben Niu, Chi Keung Simon Shiu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

3 Citations (Scopus)

Abstract

Prefetching web content by predicting users' web requests can reduce the response time of the web server and optimize the network traffic. The Markov model that is based on the conditional probability has been studied by many researchers for web access path prediction. The prediction accuracy rate can reach up to 60 to 70 percent high. However a drawback of this type of model is that as the length of the access path grows the chance of successful path matching will decrease and the model will become inapplicable. In order to preserving the applicability as well as improving the accuracy rate, we extend the model by introducing a similarity measure among access paths. Therefore, the matching process becomes less rigid and the model will be more applicable and robust to the change of the path length.

Original language	English
Title of host publication	Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
Pages	107-111
Number of pages	5
Publication status	Published - 1 Dec 2005
Event	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia Duration: 14 Sept 2005 → 16 Sept 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3683 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
Country/Territory	Australia
City	Melbourne
Period	14/09/05 → 16/09/05

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Cite this

Niu, B., & Shiu, C. K. S. (2005). Using similarity measure to enhance the robustness of Web access prediction model. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings (pp. 107-111). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI).

Niu, Ben ; Shiu, Chi Keung Simon. / Using similarity measure to enhance the robustness of Web access prediction model. Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. 2005. pp. 107-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Using similarity measure to enhance the robustness of Web access prediction model",

abstract = "Prefetching web content by predicting users' web requests can reduce the response time of the web server and optimize the network traffic. The Markov model that is based on the conditional probability has been studied by many researchers for web access path prediction. The prediction accuracy rate can reach up to 60 to 70 percent high. However a drawback of this type of model is that as the length of the access path grows the chance of successful path matching will decrease and the model will become inapplicable. In order to preserving the applicability as well as improving the accuracy rate, we extend the model by introducing a similarity measure among access paths. Therefore, the matching process becomes less rigid and the model will be more applicable and robust to the change of the path length.",

author = "Ben Niu and Shiu, {Chi Keung Simon}",

year = "2005",

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day = "1",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "107--111",

booktitle = "Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings",

note = "9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 ; Conference date: 14-09-2005 Through 16-09-2005",

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Niu, B & Shiu, CKS 2005, Using similarity measure to enhance the robustness of Web access prediction model. in Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3683 LNAI, pp. 107-111, 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005, Melbourne, Australia, 14/09/05.

Using similarity measure to enhance the robustness of Web access prediction model. / Niu, Ben; Shiu, Chi Keung Simon.
Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. 2005. p. 107-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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N2 - Prefetching web content by predicting users' web requests can reduce the response time of the web server and optimize the network traffic. The Markov model that is based on the conditional probability has been studied by many researchers for web access path prediction. The prediction accuracy rate can reach up to 60 to 70 percent high. However a drawback of this type of model is that as the length of the access path grows the chance of successful path matching will decrease and the model will become inapplicable. In order to preserving the applicability as well as improving the accuracy rate, we extend the model by introducing a similarity measure among access paths. Therefore, the matching process becomes less rigid and the model will be more applicable and robust to the change of the path length.

AB - Prefetching web content by predicting users' web requests can reduce the response time of the web server and optimize the network traffic. The Markov model that is based on the conditional probability has been studied by many researchers for web access path prediction. The prediction accuracy rate can reach up to 60 to 70 percent high. However a drawback of this type of model is that as the length of the access path grows the chance of successful path matching will decrease and the model will become inapplicable. In order to preserving the applicability as well as improving the accuracy rate, we extend the model by introducing a similarity measure among access paths. Therefore, the matching process becomes less rigid and the model will be more applicable and robust to the change of the path length.

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M3 - Conference article published in proceeding or book

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings

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Niu B, Shiu CKS. Using similarity measure to enhance the robustness of Web access prediction model. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. 2005. p. 107-111. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Using similarity measure to enhance the robustness of Web access prediction model

Abstract

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ASJC Scopus subject areas

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