摘要： PPM模型适合预测用户的下一个请求，但已有的PPM模型不具备在线性，更新通过重构来实现，不能满足实时更新的要求。该文提出基于非压缩后缀树的在线PPM预测模型，采用非压缩后缀树实现增量式在线更新，提高了模型的更新速度。该模型的优点是具备在线性。

关键词: Web预取, PPM模型, 非压缩后缀树

Abstract: Prediction by Partial Matching(PPM) models are appropriate for predicting the user’s next request, but these models are not on-line and their updates are implemented by rebuilding models which can not meet the real-time update. This paper presents an on-line PPM prediction model based on non-compact suffix tree. The model makes use of non-compact suffix tree to implement the incremental on-line update, and its update speed is improved. This model has the important property of being on-line.

Key words: Web prefetching, Prediction by Partial Matching(PPM), non-compact suffix tree

中图分类号: 

• TP311