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Mining web logs for prediction models in WWW caching and prefetching
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Source International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining table of contents
San Francisco, California
Pages: 473 - 478  
Year of Publication: 2001
ISBN:1-58113-391-X
Authors
Qiang Yang  Simon Fraser University, Burnaby, BC, Canada
Haining Henry Zhang  IBM E-business Innovation Center, Vancouver, Burnaby, BC, Canada
Tianyi Li  Simon Fraser University, Burnaby, BC, Canada
Sponsors
SIGMOD: ACM Special Interest Group on Management of Data
AAAI : American Association for Artificial Intelligence
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 21,   Downloads (12 Months): 116,   Citation Count: 25
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ABSTRACT

Web caching and prefetching are well known strategies for improving the performance of Internet systems. When combined with web log mining, these strategies can decide to cache and prefetch web documents with higher accuracy. In this paper, we present an application of web log mining to obtain web-document access patterns and use these patterns to extend the well-known GDSF caching policies and prefetching policies. Using real web logs, we show that this application of data mining can achieve dramatic improvement to web-access performance.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  25

Collaborative Colleagues:
Qiang Yang: colleagues
Haining Henry Zhang: colleagues
Tianyi Li: colleagues