.png){kind=icon}
Return
A data mining algorithm for generalized web prefetching
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2003.1232270.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Predictive Web prefetching refers to the mechanism of deducing the forthcoming page accesses of a client based on its past accesses. In this paper, we present a new context for the interpretation of Web prefetching algorithms as Markov predictors. We identify the factors that affect the performance of Web prefetching algorithms. We propose a new algorithm called WMo, which is based on data mining and is proven to be a generalization of existing ones. It was designed to address their specific limitations and its characteristics include all the above factors. It compares favorably with previously proposed algorithms. Further, the algorithm efficiently addresses the increased number of candidates. We present a detailed performance evaluation of WMo with synthetic and real data. The experimental results show that WMo can provide significant improvements over previously proposed Web prefetching algorithms.
Keywords:
prefetching
prediction
Web mining
association rules
data mining
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.4
Papers:
6.8K
Citations:
3.2W
Organization
No organization information available
Cited Papers
ShareSave
ShareSaveShareSaveShareSaveIntegrated microRNA and mRNA Transcriptome Sequencing Reveals the Potential Roles of miRNAs in Stage I Endometrioid Endometrial Carcinoma.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAIShareSave
View more