Improving the Performance of a Proxy Cache Using Tree Augmented Naive Bayes Classifier

Benadit, P. Julian; Francis, F. Sagayaraj; Muruganantham, U.

doi:10.1016/j.procs.2015.02.010

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…The normalized frequencies and duration can be easily derived using the Eqns. (10,11,15,16). The distance metrics for the interest interval is given by: Current web page access frequency K…”

Section: Multiple Factor Distance Measuresmentioning

confidence: 99%

“…Another approach includes tree augmented naive bayes(TANB) [16] algorithm gives better results and improves the performance of traditional policies like LRU and GDSF. According to the recurrent sliding window, frequency and recency of the web objects are calculated/preprocessed and fed as input to TANB to find out the page with the highest probability of revisiting in the future.…”

Section: Introductionmentioning

confidence: 99%

“…(a,c,:) wp − T (b,c,:) wp (12) ∆ Uwp [u a , u b ] = |T (a,c,:) wp − T (b,c,:) wp Locally Normalized Users' Website duration interval: This interval( ∆ x Uwpt [u a , u b ]) between the two users(u a and u b ) is calculated based on the local normalization of the website duration arrays(D (a,c,:,:) wpt and D (b,c,:,:) wpt) for a specific website(w c ). The normalized distance arrays are denoted by: ≤ j ≤ c p and 1 ≤ l ≤ c t ≤ j ≤ c p and 1 ≤ l ≤ c t(16) …”

mentioning

confidence: 99%

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An Improved Web Caching System With Locally Normalized User Intervals

Vaithinathan¹,

Benadit

Parthiban

et al. 2021

IEEE Access

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Caching is one of the most promising areas in the field of future internet architecture like Information-centric Networking, Software Defined Networking, and IoT. In Web caching, most of the web content is readily available across the network, even if the webserver is not reachable. Several existing traditional caching methods and cache replacement strategies are evaluated based on the metrics like hit ratio and byte hit Ratio. However, these metrics have not been improved over the period because of the traditional caching policies. So, in this paper, we have used an intelligent function like locally normalized intervals of page visit, website duration, users' interest between user groups is proposed. These intervals are combined with multiple distance metrics like Manhattan, squared Euclidean, and 3-,4-,5-norm Minkowski. In order to obtain significant common user navigation patterns, the clustering relation between the users using different intervals and distances is thoroughly analyzed. These patterns are successfully coupled with greedy web cache replacement strategies to improve the efficiency of the proposed web cache system. Particularly for improving the caching metrics more, we used an AI-based intelligent approach like Random Forest classifier to boost the prefetch buffer performance and achieves the maximum hit rate of 0.89, 0.90, and byte hit rate of 0.87, 0.89 for Greedy Dual Size Frequency and Weighted Greedy Dual Size Frequency algorithms, respectively. Our experiments show good hit/byte hit rates than the frequently used algorithms like least recently used and least frequently used.

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Section: Multiple Factor Distance Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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An Improved Web Caching System With Locally Normalized User Intervals

Vaithinathan¹,

Benadit

Parthiban

et al. 2021

IEEE Access

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“…Benadit et al conducted four studies in the past five years. Benadit et al proposed the tree augmented naïve bayes (TANB) to carry out the Web log classification process and future Web object predictions (Benadit and Sagayaraj Francis, 2015) then continue his research by proposing a Very Fast Decision Tree (VFDT) (Benadit et al , 2015). Both algorithms are combined with LRU and GDSF and use the same data set from IRcache.…”

Section: Caching Strategymentioning

confidence: 99%

Caching strategy for Web application – a systematic literature review

Zulfa

Hartanto

Permanasari

2020

IJWIS

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Purpose Internet users and Web-based applications continue to grow every day. The response time on a Web application really determines the convenience of its users. Caching Web content is one strategy that can be used to speed up response time. This strategy is divided into three main techniques, namely, Web caching, Web prefetching and application-level caching. The purpose of this paper is to put forward a literature review of caching strategy research that can be used in Web-based applications. Design/methodology/approach The methods used in this paper were as follows: determined the review method, conducted a review process, pros and cons analysis and explained conclusions. The review method is carried out by searching literature from leading journals and conferences. The first search process starts by determining keywords related to caching strategies. To limit the latest literature in accordance with current developments in website technology, search results are limited to the past 10 years, in English only and related to computer science only. Findings Note in advance that Web caching and Web prefetching are slightly overlapping techniques because they have the same goal of reducing latency on the user’s side. But actually, the two techniques are motivated by different basic mechanisms. Web caching uses the basic mechanism of cache replacement or the algorithm to change cache objects in memory when the cache capacity is full, whereas Web prefetching uses the basic mechanism of predicting cache objects that can be accessed in the future. This paper also contributes practical guidelines for choosing the appropriate caching strategy for Web-based applications. Originality/value This paper conducts a state-of-the art review of caching strategies that can be used in Web applications. Exclusively, this paper presents taxonomy, pros and cons of selected research and discusses data sets that are often used in caching strategy research. This paper also provides another contribution, namely, practical instructions for Web developers to decide the caching strategy.

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“…The conditional mutual information ( I p ) is used in in building TAN model. The conditional mutual information is calculated using the formula below …”

Section: Structure-learning Of Bayesian Networkmentioning

confidence: 99%

Dynamic Failure Analysis of Process Systems Using Principal Component Analysis and Bayesian Network

Adedigba

Khan

Yang

2017

Ind. Eng. Chem. Res.

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Modern industrial processes are highly instrumented with more frequent recording of data. This provides abundant data for safety analysis; however, these data resources have not been well used. This paper presents an integrated dynamic failure prediction analysis approach using principal component analysis (PCA) and the Bayesian network (BN). The key process variables that contribute the most to process performance variations are detected with PCA, while the Bayesian network is adopted to model the interactions among these variables to detect faults and predict the time-dependent probability of system failure. The proposed integrated approach uses big data analysis. The structure of BN is learned using past historical data. The developed BN is used to detect faults and estimate system failure risk. The risk is updated subsequently as new process information is collected. The updated risk is used as a decision-making parameter. The proposed approach is validated through a case of a crude oil distillation unit operation.

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Improving the Performance of a Proxy Cache Using Tree Augmented Naive Bayes Classifier

Cited by 10 publications

References 8 publications

An Improved Web Caching System With Locally Normalized User Intervals

An Improved Web Caching System With Locally Normalized User Intervals

Caching strategy for Web application – a systematic literature review

Dynamic Failure Analysis of Process Systems Using Principal Component Analysis and Bayesian Network

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