A data mining algorithm for generalized Web prefetching

• Published in: IEEE transactions on knowledge and data engineering Vol. 15; no. 5; pp. 1155 - 1169
• Main Authors: Nanopoulos, A., Katsaros, D., Manolopoulos, Y.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2003; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bandwidth; Communication system traffic control; Computer Society; Costs; Data mining; Internet; Markov processes; Network servers; Performance evaluation; Prefetching; Propagation delay; Shape control
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2003.1232270

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 WM,,, 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 WM, with synthetic and real data. The experimental results show that WM/sub o/ can provide significant improvements over previously proposed Web prefetching algorithms.