DocumentCode :
1733825
Title :
Meaningless to meaningful Web log data for generation of Web pre-caching decision rules using Rough Set
Author :
Sulaiman, Sarina ; Shamsuddin, Siti Mariyam ; Ahmad, Nor Bahiah ; Abraham, Ajith
Author_Institution :
Soft Comput. Res. Group, Univ. Teknol. Malaysia, Skudai, Malaysia
fYear :
2012
Firstpage :
91
Lastpage :
98
Abstract :
Web caching and pre-fetching are vital technologies that can increase the speed of Web loading processes. Since speed and memory are crucial aspects in enhancing the performance of mobile applications and websites, a better technique for Web loading process should be investigated. The weaknesses of the conventional Web caching policy include meaningless information and uncertainty of knowledge representation in Web logs data from the proxy cache to mobile-client. The organisation and learning task of the knowledge-processing for Web logs data require explicit representation to deal with uncertainties. This is due to the exponential growth of rules for finding a suitable knowledge representation from the proxy cache to the mobileclient. Consequently, Rough Set is chosen in this research to generate Web pre-caching decision rules to ensure the meaningless Web log data can be changed to meaningful information.
Keywords :
Internet; cache storage; data mining; knowledge representation; rough set theory; uncertainty handling; Web loading process; Web log mining; Web precaching decision rules generation; Web prefetching; knowledge representation uncertainty; meaningful Web log data; meaningless Web log data; meaningless information; mobile-client; proxy cache; rough set; Accuracy; Classification algorithms; Prediction algorithms; Servers; Testing; Training; Uncertainty; decision rules; rough set; web caching; web log data; web pre-fetching;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining and Optimization (DMO), 2012 4th Conference on
Conference_Location :
Langkawi
Print_ISBN :
978-1-4673-2717-6
Type :
conf
DOI :
10.1109/DMO.2012.6329804
Filename :
6329804
Link To Document :
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