Title :
Neural nets based predictive prefetching to tolerate WWW latency
Author :
Ibrahim, Tamer I. ; Xu, Cheng-Zhong
Author_Institution :
Dept. of Electr. & Comput. Eng., Wayne State Univ., Detroit, MI, USA
Abstract :
With the explosive growth of WWW applications on the Internet, users are experiencing access delays more often than ever. Recent studies showed that prefetching could alleviate the WWW latency to a larger extent than caching. Existing prefetching methods are mostly based on URL graphs. They use the graphical nature of hypertext links to determine the possible paths through a hypertext system. While they have been demonstrated effective in prefetching of documents that are often accessed, they are incapable of pre-retrieving documents whose URLs had never been accessed. We propose a context-specific prefetching technique to overcome the limitation. It relies on keywords in anchor texts of URLs to characterize user access patterns and on neural networks over the keyword set to predict future requests. It features a self-learning capability and good adaptivity to the change of user surfing interest. The technique was implemented in a SmartNewsReader system and cross-examined in a daily browsing of MSNBC and CNN news sites. The experimental results showed an achievement of approximately 60% hit ratio due to prefetching. Of the prefetched documents, less than 30% was undesired
Keywords :
Internet; hypermedia; neural nets; query processing; storage management; Internet; SmartNewsReader system; WWW applications; WWW latency; access delays; anchor texts; context-specific prefetching technique; hypertext links; keywords; neural networks; predictive prefetching; self-learning capability; user access patterns; user surfing interest; Application software; Delay; Etching; Explosives; History; Internet; Neural networks; Prefetching; Uniform resource locators; World Wide Web;
Conference_Titel :
Distributed Computing Systems, 2000. Proceedings. 20th International Conference on
Conference_Location :
Taipei
Print_ISBN :
0-7695-0601-1
DOI :
10.1109/ICDCS.2000.840980