Abstract
Users who visit a web page repeatedly at frequent intervals are more interested in knowing the recent changes that have occurred on the page than the entire contents of the web page. Because of the increased dynamism of web pages, it would be difficult for the user to identify the changes manually. This paper proposes an enhanced model for detecting changes in the pages, which is called CaSePer (Change detection based on Segmentation with Personalization). The change detection is micro-managed by introducing web page segmentation. The web page change detection process is made efficient by having it perform a dual-step process. The proposed method reduces the complexity of the change-detection by focusing only on the segments in which the changes have occurred. The user-specific personalized change detection is also incorporated in the proposed model. The model is validated with the help of a prototype implementation. The experiments conducted on the prototype implementation confirm a 77.8% improvement and a 97.45% accuracy rate.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of King Saud University - Computer and Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
