Abstract
The current research of Prediction by Partial Match(PPM)model generally focuses on the reduction of space complexity of the model under the condition of guaranteeing the prediction accuracy.But most of the studies lack the adaptive mechanism,which is requisite in on-line systems.In terms of Web access characteristics,popularity based adaptive PPM prediction model(PA PPM)was proposed,whose core was prefetching algorithm based on Web objects' popularity.PA PPM actualized dynamic adaptive Web prefetching by three parts:model construction,model prediction and model update.The mechanisms of deterministic context prediction,optimal order estimation and LRU based discarding policy to support adaptation were discussed and realized.Under the condition of integrated Web caching and prefetching,experimental results have shown that PA PPM model can achieve a good performance and can be used to realize on-line Web prefetching.
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