Research Article | OPEN ACCESS
Combining Pre-fetching and Intelligent Caching Technique (SVM) to Predict Attractive Tourist Places
1K.R. Baskaran and 2C. Kalaiarasan
1Department of Information Technology, Kumaraguru College of Technology, Coimbatore, India
2Tamilnadu College of Engineering, Coimbatore, India
Research Journal of Applied Sciences, Engineering and Technology 2015 1:40-46
Received: July 01, 2014 | Accepted: August 26, 2014 | Published: January 05, 2015
Abstract
Combining Web caching and Web pre-fetching techniques results in obtaining the required information almost instantaneously. It also results in improved bandwidth utilization, load reduction on the origin server and reduces access delay. Web Pre-fetching is the process of fetching some of the predicted Web pages in advance which is assumed to be used by the user in the near future and the caching is the process of storing the pre-fetched Web pages in the cache memory. In the literature many interesting works have been reported separately for Web caching and for Web pre-fetching. In this study we combine pre-fetching (using clustering) and caching (using SVM) to keep track of the tourist spots that are likely to be visited by the tourists in the near future based on the previous history of visits. With the help of real data it is demonstrated that our approach is superior than clustering based pre-fetching technique using traditional LRU based caching policy which does not use SVM.
Keywords:
Classification, clustering, confidence , hit-ratio , support , Support Vector Machine (SVM),
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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