pART2: using adaptive resonance theory for web caching prefetching

As the Web becomes the major source for information and services, fast access to relevant Web objects is a critical requirement for many applications. Various methods have been developed to achieve this goal. Web page prefetching is a commonly used technique that is highly effective in reducing user...

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Bibliographic Details
Published in:Neural computing & applications 2017-12, Vol.28 (Suppl 1), p.1275-1288
Main Authors: Feng, Wenying, Kazi, Toufiq Hossain, Hu, Gongzhu, Huang, Jimmy Xiangji
Format: Article
Language:English
Subjects:
Adaptive systems
Artificial Intelligence
Caching
Clustering
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer simulation
Data Mining and Knowledge Discovery
Digital systems
Image Processing and Computer Vision
Monte Carlo simulation
Original Article
Parameter sensitivity
Probability and Statistics in Computer Science
Sensitivity analysis
Statistical analysis
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Summary:As the Web becomes the major source for information and services, fast access to relevant Web objects is a critical requirement for many applications. Various methods have been developed to achieve this goal. Web page prefetching is a commonly used technique that is highly effective in reducing user perceived delays. In this paper, we propose a new prefetching model pART2, which is based on the adaptive resonance theory (ART) for data clustering. A corresponding cache replacement policy (Probability-Based Replacement) is also proposed and developed. The new policy matches with the prefetching scheme and therefore produces a higher cache hit ratio compared with some of the traditional algorithms. To evaluate the new model, we conduct a series of experiments using data sets collected from a digital library system and Monte Carlo simulation techniques. Sensitivity of the parameters and statistical analysis are also presented. The proposed model using ART-type networks provides a promising avenue for constructing accurate caching prefetching systems that are flexible and adaptive.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-017-3173-7