A case study on memory efficient prediction models for web prefetching

Gracia, Chithra D.; Selvaraj, S.

doi:10.1109/icetets.2016.7603008

Cited by 6 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the literature [23,24], performance of prediction is measured in terms of two major performance metrics: precision and hit ratio. These metrics have also been utilized to assess the precision of prediction in our own study, where:…”

Section: Performance Evaluationmentioning

confidence: 99%

Semantically Enriched Keyword Prefetching Based on Usage and Domain Knowledge

Setia,

Jyoti,

Duhan

et al. 2024

JWE

View full text Add to dashboard Cite

In intelligent web systems [2], web prefetching [27] plays a crucial role. In order to make accurate predictions for web prefetching, it is important but challenging to uncover valuable information from web use statistics [16]. Using statistics and domain expertise, this study presents a new approach dubbed SPUDK for efficient prefetching. In this paper, it is shown how web access logs can be used efficiently for browsing prediction. Our main focus is on the technique needed to manage the queries found in web access logs so that valuable information can be attained. We further process these access logs using a taxonomy and a thesaurus, WordNet, to find the semantics of queries. SPUDK, a system that organises use data into semantic clusters, is one example of this approach. Our contributions in this paper are as follows: (1) A technique to exploit query keywords from access logs. (2) An approach to enrich queries with semantic information. (3) A new similarity measure for finding similarity among URLs present in access logs. (4) A novel clustering technique to find semantic clusters of URLs. (5) Experimental evaluation of the proposed system. The proposed SPUDK system is evaluated using American Online (AOL) logs, which gives improvement of 39% in precision of prediction, 35% in hit ratio and reduction of 50.6% in latency on average as compared to other prediction techniques in the literature.

show abstract

Section: Performance Evaluationmentioning

confidence: 99%

Semantically Enriched Keyword Prefetching Based on Usage and Domain Knowledge

Setia,

Jyoti,

Duhan

et al. 2024

JWE

View full text Add to dashboard Cite

show abstract

“…Prediction by partial match [15,16,33] e PPM model uses a set of previous objects to predict the next item in a particular stream It is a restricted version of Markov chain that provides prediction based on the only selected set of objects and selection of a right set of objects is a very challenging task, so this kind of vision is not also; it limits the result as it does not cover all the objects, thereby ruling it out of the scope of current work…”

Section: Proposed Hybrid Modelmentioning

confidence: 99%

“…In literature [33,36], prediction performance is measured using two primary Input: N-gram associated click-graph (NC-graph) Output: weighted N-grams corresponding to distinct URLs stored in matrix WL Begin (1) Create a matrix WL of order m × n//m ⟶ no. of distinct N-grams of all the queries of PL and n ⟶ no.…”

Section: Performance Evaluationmentioning

confidence: 99%

HPM: A Hybrid Model for User’s Behavior Prediction Based on N-Gram Parsing and Access Logs

Setia

Verma

Duhan

2020

Scientific Programming

View full text Add to dashboard Cite

The continuous growth of the World Wide Web has led to the problem of long access delays. To reduce this delay, prefetching techniques have been used to predict the users’ browsing behavior to fetch the web pages before the user explicitly demands that web page. To make near accurate predictions for users’ search behavior is a complex task faced by researchers for many years. For this, various web mining techniques have been used. However, it is observed that either of the methods has its own set of drawbacks. In this paper, a novel approach has been proposed to make a hybrid prediction model that integrates usage mining and content mining techniques to tackle the individual challenges of both these approaches. The proposed method uses N-gram parsing along with the click count of the queries to capture more contextual information as an effort to improve the prediction of web pages. Evaluation of the proposed hybrid approach has been done by using AOL search logs, which shows a 26% increase in precision of prediction and a 10% increase in hit ratio on average as compared to other mining techniques.

show abstract

“…Gracia and Sudha (2016) proposed a Web prefetching method by modifying the Markov-model, which used too much memory. Therefore, this study adopted MePPM MeLRS methods and modifies the Markov-model, to make memory usage more efficient.…”

Section: Caching Strategymentioning

confidence: 99%

“…Research (Holmqvist et al , 2019) used a private data set provided by Bison, a company that collaborates with the research. Other data sets such as log files from the Digital Library used in Feng et al (2017), university websites used in Wan et al (2012) and Gracia and Sudha (2016) are private data sets that cannot explain in this paper. The log data set from proxy or Web server has the same character.…”

Section: Data Setmentioning

confidence: 99%

Caching strategy for Web application – a systematic literature review

Zulfa

Hartanto

Permanasari

2020

IJWIS

View full text Add to dashboard Cite

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.

show abstract

A case study on memory efficient prediction models for web prefetching

Cited by 6 publications

References 14 publications

Semantically Enriched Keyword Prefetching Based on Usage and Domain Knowledge

Semantically Enriched Keyword Prefetching Based on Usage and Domain Knowledge

HPM: A Hybrid Model for User’s Behavior Prediction Based on N-Gram Parsing and Access Logs

Caching strategy for Web application – a systematic literature review

Contact Info

Product

Resources

About