A maximum entropy approach to natural language processing
Computational Linguistics
Using predictive prefetching to improve World Wide Web latency
ACM SIGCOMM Computer Communication Review
Web prefetching between low-bandwidth clients and proxies: potential and performance
SIGMETRICS '99 Proceedings of the 1999 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Extended application of suffix trees to data compression
DCC '96 Proceedings of the Conference on Data Compression
WhatNext: A Prediction System for Web Requests using N-gram Sequence Models
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1 - Volume 1
A Data Mining Algorithm for Generalized Web Prefetching
IEEE Transactions on Knowledge and Data Engineering
A Keyword-Based Semantic Prefetching Approach in Internet News Services
IEEE Transactions on Knowledge and Data Engineering
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
Objective-Optimal Algorithms for Long-Term Web Prefetching
IEEE Transactions on Computers
Mining longest repeating subsequences to predict world wide web surfing
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
Reducing file system latency using a predictive approach
USTC'94 Proceedings of the USENIX Summer 1994 Technical Conference on USENIX Summer 1994 Technical Conference - Volume 1
Using current web page structure to improve prefetching performance
Computer Networks: The International Journal of Computer and Telecommunications Networking
A framework of online proxy-based web prefetching
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
| Hi-index | 0.00 |
Web prefetching is a primary means to reduce user access latency. An important amount of work can be found by the use of PPM (Prediction by Partial Match) for modeling and predicting user request patterns in the open literature. However, in general, existing PPM models are constructed off-line. It is highly desirable to perform the online update of the PPM model incrementally because user request patterns may change over time. We present an online PPM model to capture the changing patterns and fit the memory. This model is implemented based on a noncompact suffix tree. Our model only keeps the most recent W requests using a sliding window. To further improve the prefetching performance, we make use of maximum entropy principle to model for the outgoing probability distributions of nodes. Our prediction model combines entropy, prediction accuracy rate and the longest match rule. A performance evaluation is presented using real web logs. Trace-driven simulation results show our PPM prediction model can provide significant improvements over previously proposed models.