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Article: A Data Cube Model for Prediction-based Web Prefetching

TitleA Data Cube Model for Prediction-based Web Prefetching
Authors
KeywordsAlgorithms
Buffer storage
Correlation methods
Data mining
Data reduction
Issue Date2003
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0925-9902
Citation
Journal of Intelligent Information Systems, 2003, v. 20 n. 1, p. 11-30 How to Cite?
AbstractReducing the web latency is one of the primary concerns of Internet research. Web caching and web prefetching are two effective techniques to latency reduction. A primary method for intelligent prefetching is to rank potential web documents based on prediction models that are trained on the past web server and proxy server log data, and to prefetch the highly ranked objects. For this method to work well, the prediction model must be updated constantly, and different queries must be answered efficiently. In this paper we present a data-cube model to represent Web access sessions for data mining for supporting the prediction model construction. The cube model organizes session data into three dimensions. With the data cube in place, we apply efficient data mining algorithms for clustering and correlation analysis. As a result of the analysis, the web page clusters can then be used to guide the prefetching system. In this paper, we propose an integrated web-caching and web-prefetching model, where the issues of prefetching aggressiveness, replacement policy and increased network traffic are addressed together in an integrated framework. The core of our integrated solution is a prediction model based on statistical correlation between web objects. This model can be frequently updated by querying the data cube of web server logs. This integrated data cube and prediction based prefetching framework represents a first such effort in our knowledge.
Persistent Identifierhttp://hdl.handle.net/10722/225165
ISSN
2015 Impact Factor: 1.0
2015 SCImago Journal Rankings: 0.691

 

DC FieldValueLanguage
dc.contributor.authorYang, Q-
dc.contributor.authorHuang, JZ-
dc.contributor.authorNg, KP-
dc.date.accessioned2016-04-26T07:43:44Z-
dc.date.available2016-04-26T07:43:44Z-
dc.date.issued2003-
dc.identifier.citationJournal of Intelligent Information Systems, 2003, v. 20 n. 1, p. 11-30-
dc.identifier.issn0925-9902-
dc.identifier.urihttp://hdl.handle.net/10722/225165-
dc.description.abstractReducing the web latency is one of the primary concerns of Internet research. Web caching and web prefetching are two effective techniques to latency reduction. A primary method for intelligent prefetching is to rank potential web documents based on prediction models that are trained on the past web server and proxy server log data, and to prefetch the highly ranked objects. For this method to work well, the prediction model must be updated constantly, and different queries must be answered efficiently. In this paper we present a data-cube model to represent Web access sessions for data mining for supporting the prediction model construction. The cube model organizes session data into three dimensions. With the data cube in place, we apply efficient data mining algorithms for clustering and correlation analysis. As a result of the analysis, the web page clusters can then be used to guide the prefetching system. In this paper, we propose an integrated web-caching and web-prefetching model, where the issues of prefetching aggressiveness, replacement policy and increased network traffic are addressed together in an integrated framework. The core of our integrated solution is a prediction model based on statistical correlation between web objects. This model can be frequently updated by querying the data cube of web server logs. This integrated data cube and prediction based prefetching framework represents a first such effort in our knowledge.-
dc.languageeng-
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0925-9902-
dc.relation.ispartofJournal of Intelligent Information Systems-
dc.rightsThe final publication is available at Springer via http://dx.doi.org/[insert DOI]-
dc.subjectAlgorithms-
dc.subjectBuffer storage-
dc.subjectCorrelation methods-
dc.subjectData mining-
dc.subjectData reduction-
dc.titleA Data Cube Model for Prediction-based Web Prefetching-
dc.typeArticle-
dc.identifier.emailHuang, JZ: jhuang@eti.hku.hk-
dc.identifier.emailNg, KP: kkpong@hkusua.hku.hk-
dc.identifier.doi10.1023/A:1020990805004-
dc.identifier.hkuros76494-
dc.identifier.volume20-
dc.identifier.issue1-
dc.identifier.spage11-
dc.identifier.epage30-
dc.publisher.placeUnited States-

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