Enhancing Greedy Web Proxy caching using Weighted Random Indexing based Data Mining Classifier
| Autor: | Julian Benadit Pernabas, Krishna Kumar Vaithinathan, Sagayraj Francis Fidele |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
020206 networking & telecommunications 02 engineering and technology QA75.5-76.95 Management Science and Operations Research computer.software_genre Proxy server Computer Science Applications Random indexing Server Electronic computers. Computer science 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Web proxy Proxy (statistics) Cluster analysis computer Classifier (UML) Information Systems |
| Zdroj: | Egyptian Informatics Journal, Vol 20, Iss 2, Pp 117-130 (2019) |
| ISSN: | 1110-8665 |
| Popis: | Web Proxy caching system is an intermediary between the Web users and servers that try to alleviate the loads on the origin servers by caching particular Web objects and behaves as the proxy for the server and services the requests that are made to the servers. In this paper, the performance of a Proxy system is measured by the number of hits at the Proxy. Higher number of hits at the Proxy server reflects the effectiveness of the Proxy system. The number of hits is determined by the replacement policies chosen by the Proxy systems. Traditional replacement policies that are based on time and size are reactive and do not consider the events that will possibly happen in the future. The performance of the web proxy caching system is improved by adapting Data Mining Classifier model based on Web User clustering and Weighted Random Indexing Methods. The outcome of the paper are proactive strategies that augment the traditional replacement policies such as GDS, GDSF, GD∗ which uses the Data Mining techniques. Keywords: GDS, GDSF, GD∗, Random indexing, Clustering, Proxy, Data Mining |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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