INTELLIGENT WEB CACHING USING MACHINE LEARNING METHODS

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F11%3A86081465" target="_blank" >RIV/61989100:27740/11:86081465 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

INTELLIGENT WEB CACHING USING MACHINE LEARNING METHODS

Popis výsledku v původním jazyce

Web caching is a technology to improve network traffic on the Internet. It is a temporary storage of Web objects for later retrieval. Three significant advantages of Web caching include reduction in bandwidth consumption, server load, and latency. Theseadvantages make the Web to be less expensive yet it provides better performance. This research aims to introduce an advanced machine learning method for a classification problem in Web caching that requires a decision to cache or not to cache Web objectsin a proxy cache server. The challenges in this classification problem include the issues in identifying attributes ranking and improve the classification accuracy significantly. This research includes four methods that are Classification and RegressionTrees (CART), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF) and Tree Net (TN) for classification on Web caching. The experimental results reveal that CART performed extremely well in classifying Web objects from the

Název v anglickém jazyce

INTELLIGENT WEB CACHING USING MACHINE LEARNING METHODS

Popis výsledku anglicky

Web caching is a technology to improve network traffic on the Internet. It is a temporary storage of Web objects for later retrieval. Three significant advantages of Web caching include reduction in bandwidth consumption, server load, and latency. Theseadvantages make the Web to be less expensive yet it provides better performance. This research aims to introduce an advanced machine learning method for a classification problem in Web caching that requires a decision to cache or not to cache Web objectsin a proxy cache server. The challenges in this classification problem include the issues in identifying attributes ranking and improve the classification accuracy significantly. This research includes four methods that are Classification and RegressionTrees (CART), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF) and Tree Net (TN) for classification on Web caching. The experimental results reveal that CART performed extremely well in classifying Web objects from the

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

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Svazek periodika

21

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

24

Strana od-do

429-452

Kód UT WoS článku

000297179900004

EID výsledku v databázi Scopus

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