Predictive Web Prefetching: A Combined Approach Using Clustering Algorithms and WEKA in High-Traffic Settings

Research summary

The summary, key findings, methodology and relevance notes below are this website’s own description of the paper, written from the published abstract and text. The official abstract and citation details are given further down.

The Problem

Conventional web prefetching assumes servers have idle time, an assumption that breaks down in high-traffic, congested network environments.

The Methodology

Design and evaluation of a web prefetching model: preprocessing of server log files, construction of an online navigation graph, clustering with the WEKA machine learning toolkit, and comparison against conventional prefetching techniques on domain client-group logs.

The Core Finding

A prefetching approach that builds an online navigation graph from server logs and clusters navigation behaviour with WEKA outperformed conventional prefetching techniques on domain client-group logs, particularly once server idle time allowed prefetching across all user predictions.

The Citation

Ajibesin, A. A., Vajjhala, N. R., Joel, E., & Rakshit, S. (2025). Predictive Web Prefetching: A Combined Approach Using Clustering Algorithms and WEKA in High-Traffic Settings. In Frank Lin, David Pastor, Nishtha Kesswani, Ashok Patel, Sushanta Bordoloi, Chaitali Koley (Eds.), Artificial Intelligence in Internet of Things (IoT): Key Digital Trends: Proceedings of 8th International Conference on Internet of Things and Connected Technologies (ICIoTCT 2023) (pp. 221–231). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-5786-2_17 Copy APA

What question does this paper answer?

How can web prefetching be improved for high-traffic, congested network environments in which servers have little idle time?

What did the study find, in detail?

The authors propose a prefetching approach that builds an online navigation graph from preprocessed web server log data and uses clustering (with the WEKA toolkit) to predict the next related web items a user will request. Evaluated on log files from specific domain client groups, the clustering method outperformed conventional prefetching techniques, particularly after a short-lived acceleration phase once server idle time was sufficient to prefetch across all user predictions.

Why does it matter?

Network congestion reduces the usefulness of information because pages load slowly. A prefetching method that works when server idle time is scarce, and that combines with web caching, is directly applicable to busy web services and IoT-connected environments.

Study at a glance

Abstract

Network congestion poses challenges for internet users, diminishing the utility of accessed information due to reduced speeds. To mitigate this, various strategies such as web mining, caching, and server prefetching have been proposed to enhance internet performance. We introduced a novel prefetching approach tailored for high-traffic environments with minimal server idle times. This method constructs an online navigation graph from preprocessed log data, offering insights into user navigation patterns across the web. Our primary objective was to refine web prefetching techniques for congested digital landscapes. We evaluated the model using log files from specific domain client groups. Our proposed web clustering method surpasses conventional prefetching techniques, especially after a short-lived acceleration when server idle times are sufficient for prefetching across all user predictions. This method, when synergized with web caching, can predict subsequent related web items following a particular web object request.

Abstract as published in Artificial Intelligence in Internet of Things (IoT): Key Digital Trends: Proceedings of 8th International Conference on Internet of Things and Connected Technologies (ICIoTCT 2023).

Keywords: Predictive web prefetching; Network congestion; Clustering algorithms; WEKA toolkit; High-traffic environments; Online navigation graph; Server idle times; Web caching synergy; Domain client groups

Limitations

• The evaluation used log files from specific domain client groups; the abstract does not report results for other traffic profiles.
• The advantage is described as emerging after a short-lived acceleration phase, i.e. it depends on sufficient server idle time.

When this research may be relevant

This paper may be relevant to researchers working on web prefetching and caching, web usage mining and navigation-pattern prediction, clustering-based prediction with WEKA, and performance of web services under congestion or in IoT settings.

Research topics addressed: web prefetching; web caching; network congestion; clustering algorithms; WEKA; web usage mining; web log analysis; user navigation patterns; Internet performance; Internet of Things

How to cite

Ajibesin, A. A., Vajjhala, N. R., Joel, E., & Rakshit, S. (2025). Predictive Web Prefetching: A Combined Approach Using Clustering Algorithms and WEKA in High-Traffic Settings. In Frank Lin, David Pastor, Nishtha Kesswani, Ashok Patel, Sushanta Bordoloi, Chaitali Koley (Eds.), Artificial Intelligence in Internet of Things (IoT): Key Digital Trends: Proceedings of 8th International Conference on Internet of Things and Connected Technologies (ICIoTCT 2023) (pp. 221–231). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-5786-2_17

@inproceedings{ajibesin2025predictive,
  title = {Predictive Web Prefetching: A Combined Approach Using Clustering Algorithms and WEKA in High-Traffic Settings},
  author = {Ajibesin, Adeyimi Abel and Vajjhala, Narasimha Rao and Joel, Ernest and Rakshit, Sandip},
  booktitle = {Artificial Intelligence in Internet of Things (IoT): Key Digital Trends: Proceedings of 8th International Conference on Internet of Things and Connected Technologies (ICIoTCT 2023)},
  series = {Lecture Notes in Networks and Systems},
  editor = {Frank Lin and David Pastor and Nishtha Kesswani and Ashok Patel and Sushanta Bordoloi and Chaitali Koley},
  pages = {221--231},
  year = {2025},
  publisher = {Springer Nature Singapore},
  doi = {10.1007/978-981-97-5786-2_17},
  url = {https://doi.org/10.1007/978-981-97-5786-2_17}
}