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Analyzing and Optimizing the Usability of Website Access

Analyzing and Optimizing the Usability of Website Access

Sathiyamoorthi V., Jayapandian N., Gnana Prakasi O. S., Kanmani P., Revathi Vaithiyanathan, Prasanth Rao A.
Copyright: © 2020 |Volume: 12 |Issue: 2 |Pages: 19
ISSN: 1938-0194|EISSN: 1938-0208|EISBN13: 9781799805847|DOI: 10.4018/IJWP.2020070102
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MLA

Sathiyamoorthi V., et al. "Analyzing and Optimizing the Usability of Website Access." IJWP vol.12, no.2 2020: pp.22-40. http://doi.org/10.4018/IJWP.2020070102

APA

Sathiyamoorthi V., Jayapandian N., Gnana Prakasi O. S., Kanmani P., Vaithiyanathan, R., & Prasanth Rao A. (2020). Analyzing and Optimizing the Usability of Website Access. International Journal of Web Portals (IJWP), 12(2), 22-40. http://doi.org/10.4018/IJWP.2020070102

Chicago

Sathiyamoorthi V., et al. "Analyzing and Optimizing the Usability of Website Access," International Journal of Web Portals (IJWP) 12, no.2: 22-40. http://doi.org/10.4018/IJWP.2020070102

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Abstract

The world wide web (WWW) plays a significant role in information sharing and distribution. In web-based information access, the speed of information retrieval plays a critical role in shaping the web usability and determining the user satisfaction in accessing webpages. To deal with this problem, web caching is used. The problem with the present web caching system is that it is very hard to recognize webpages that are to be accessed and then to be cached. This is forced by the fact that there are broad categories of users and each one having their own preferences. Hence, it is decided to propose a novel approach for web access pattern generation by analyzing the web log file present in the proxy server. Further, it tries to propose a novel hybrid policy called popularity-aware modified least frequently used (PMLFU) that best suits for the current proxy-based web caching environment. It combines features such as frequency, recency, popularity, and user page count in decision-making policy. The performance of the proposed system is observed using real-time datasets, empirically using IRCACHE datasets.

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