Web Prefetching Research Articles

Semantically Enriched Keyword Prefetching Based on Usage and Domain Knowledge

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.

Enhancing Accessibility to Data in Data-Intensive Web Applications by Using Intelligent Web Prefetching Methodologies

Data-intensive Web Applications built using client–server architectures usually provide prefetching mechanisms to enhance data accessibility. Prefetching is a strategy of retrieving data before it is requested so that it can be ready when the user requests it. Prefetching reduces the load on the web server by making data available before the user requests it. Prefetching can be used for static content, such as images and web pages, as well as dynamic content, such as search results. Prefetching can also be used to improve the performance of web applications, as the data is available quickly. There are several scheduling methods, such as time-based scheduling, event-based scheduling and priority-based scheduling, for prefetching to ensure that essential data is always ready when the user requests it. In this study, we focus on time-based scheduling for prefetching. We introduce time-based scheduling methodologies using sequential pattern mining techniques and long-term short memory-based deep learning strategies. To show the usefulness of these strategies, we develop a prototype application. We conduct an extensive experimental study to evaluate the performance of the proposed time-based scheduling methodologies using both performance and accuracy metrics. Based on the computed metrics, using proposed prefetching methods provided a promising cache hit rate when using the optimal cache size. The results show that the proposed prefetching methodologies are useful in data-intensive web applications for enhancing data accessibility. Work remains to investigate the use of attention-based sequence-to-sequence models in the web prefetching domain.

Visibility graph analysis of web server log files

Web servers store every event in the form of logs, which contain the retrieved URL, client IP address, access time, HTTP status code, etc. There are several useful methods to analyze these log files, which are mainly based on sequential text mining techniques for applications like prefetching or driving critical information about system’s security. Finding ways to convert web server log files into graph structures may open new horizons in complex network analysis for investigation, comparison, and prediction. Since visibility graph has various successful usages for analyzing different time series data, web server log files as a kind of time-series data have the potential to be converted into visibility graph. In this research, we propose a novel method to convert web server log files into horizontal visibility graphs. Afterward, we demonstrate the result of the method on two popular datasets, NASA and Online Judge web server log files, and perform exploratory and visibility graph analysis techniques like centrality measures computation and community detection to show the promising future for the research. Moreover, we introduce a novel algorithm for a common application in web server log file analysis, web prefetching, based on a modified version of link prediction on the extracted visibility graph, and evaluate it based on AUC assessment and propose the next page to prefetch in each dataset. Finally, we propose several choices to extend the research in case of technical and practical aspects.

CIP- Efficient Method for Mining Frequent Itemsets from Data streams using Landmark Window Model

Continuous stream transactions like network monitoring, retail market data analysis and stock market prediction need the “frequent patterns” to be detected recurrently. Literature suggests that several pattern mining solutions are being developed over years. Still lot of challenges need to be addressed due to rapidness in generation of continuous, unbounded and ordered data real time. Hence extraction of frequent patterns from recent data will improve the analysis of stream data. In this article, a new landmark window model CIP (candidate indexing and pruning) is considered for mining the datasets. CIP allows us to mine over entire history of data streams, which improves the accuracy. This article also proposes the candidate indexed sub (CIS)-tree scheme to extract the essential information from each incoming transactions of data streams. Our proposal is compared with the existing “improved data stream mining” (ISDM) for maximal frequent itemsets algorithm. Extensive experimental analyses prove the superiority of the proposed CIP over the popular ISDM in terms of accuracy and time complexity for high-speed data stream. This article also covers up a case study where the proposed approach is applied for an application called “web prefetching”.

Web Usage Mining by Neural Hybrid Prediction with Markov Chain Components

This paper presents and evaluates a two-level web usage prediction technique, consisting of a neural network in the first level and contextual component predictors in the second level. We used Markov chains of different orders as contextual predictors to anticipate the next web access based on specific web access history. The role of the neural network is to decide, based on previous behaviour, whose predictor’s output to use. The predicted web resources are then prefetched into the cache of the browser. In this way, we considerably increase the hit rate of the web browser, which shortens the load times. We have determined the optimal configuration of the proposed hybrid predictor on a real dataset and compared it with other existing web prefetching techniques in terms of prediction accuracy. The best configuration of the proposed neural hybrid method provides an average web access prediction accuracy of 86.95%.

Predictive Prefetching Based on User Interaction for Web Applications

Web prefetching is a key technology to hide network latencies from users. Conventional prefetching methods, however, misconstrue the purpose of user's browsing behaviors and resulting experience due to their dependence on statistical characteristics or metadata of individual Web applications. In this letter, we propose a predictive prefetching scheme, WebPrefetcher, which utilizes interaction events to decipher user's genuine intention and context. Our intensive performance analysis results obtained with a real Web browser demonstrate that WebPrefetcher improves user-perceived quality of experience noticeably, outperforming competitive models.

Caching strategy for Web application – a systematic literature review

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.

ANALYSIS END USER BEHAVIOR OVER WEB SCENARIO FOR EFFICIENT PREFETCHING THROUGH BIG DATA ANALYSIS

Web caching and restoration are the most common techniques that play a key role in improving web performance by keeping web objects that are likely to be visited in the near future closer to the custo mer. Web caching can work independently or in combination with Web pre-fetching. Cache and Web fetching can complement each other as the temporary Log file is a crucial part of web application. The log analysis is an important issue for the web application. Log file is not to be over emphasized as a source of information in systems and network management, whereas conduct efficient investigation and gathering of useful information need to correlate different log file. Task of analyzing event log files with the ever-increasing size and complexity of today's event logs has become cumbersome to carry out manually. Nowadays latest spotlight is automatic analysis of these logs files. Analysis of Web log is an innovative and unique domain constantly formed and modified by the convergence of several emerging web technologies. Because of its interdisciplinary nature, the diversity of issues addressed, the variety and the number of Web applications, is subject to many different methodologies. Proposed methodology has been used as effective web pre-fetching technique for frequent page generation by using relative position by inter session gap. The fast and efficient web pre-fetching scheme has improved user response of web page and expedites users visiting speed. Keywords: Web mining, web caching, web prefetching, Clustering, Client server Architecture, Log File.

Improved Integrated Approach of Web Prefetching and Caching using eLRU

Improved Integrated Approach of Web Prefetching and Caching using eLRU

A Survey on Web Pre-Fetching and Web Caching Techniques for Latency Optimization in Mobile Environment

A Survey on Web Pre-Fetching and Web Caching Techniques for Latency Optimization in Mobile Environment

Usability testing on intelligent mobile web pre-fetching of cloud storage scheme

Mobile device and Cloud Storage (CS) represent the trends of technology usage of the last few years. However, the difficulty in managing the data when there are too many simultaneous uses of cloud storage services at the same time that can cause latency or delayed time. This paper evaluates mobile cloud storage services using usability testing, which is intended to access by multiple of Cloud Storage Services (CSS) with the proposed Intelligent Mobile Web Pre-fetching of Cloud Storage Scheme (MOBICS). The results show most of the respondents with 95.65% agreeing that MOBICS system was very practical and has enhanced the speed in accessing and storing data by Mobile Cloud Storage (MCS). Besides, MOBICS reduces time of interaction up to 19.28% for the local pre-fetching and 18.80% for the intelligent pre-fetching.

A Novel Probability based Approach for Optimized Prefetching

As the World Wide Web carries on to grow up rapidly in size and popularity, web traffic and network bottlenecks are more important issues in the networked world. The continued enhancement in demand for items on the World Wide Web causes severe overloading in many sites, network congestion, delay in perceived latency and network bottleneck. Many users have no patience in waiting more than a few seconds for downloading a web page, that's why Web traffic reduction system is very necessary in today World Wide Web for accessing the websites efficiently with the facility of existing networks. Web caching is an effective method to improve the performance of the World Wide Web but in today's World Wide Web caching method alone is not enough because of World Wide Web has grown quickly from a simple information-sharing mechanism to a rich collection of dynamic objects and multimedia data. The web prefetching is used to improve the performance of the proxy server. Prefetching predict web object that is expected to be requested in the near future and store them in advance, thus the response time of the user request is reduced. To improve the performance of the proxy server, this paper proposed a new framework which combines the caching system and prefetching technique and also optimize the prefetching with the help of probability. In this paper, we use the dataset for the experiment which is collected from ircache.net proxy server and give the result with the comparison of other technique of prefetching.

A Comprehensive Survey on Importance of Web Caching and Pre-Fetching

A Comprehensive Survey on Importance of Web Caching and Pre-Fetching

Improving the Performance of an Information Retrieval System through WEB Mining

AbstractIt is generally observed throughout the world that in the last two decades, while the average speed of computers has almost doubled in a span of around eighteen months, the average speed of the network has doubled merely in a span of just eight months!. In order to improve the performance, more and more researchers are focusing their research in the field of computers and its related technologies. World Wide Web (WWW) acts as a medium for sharing of information. As a result, millions of applications run on the Internet and cause increased network traffic and put a great demand on the available network infrastructure. The slow retrieval of Web pages may reduce the user interest from accessing them. To deal with this problem Web caching and Web pre-fetching are used. This paper focuses on a methodology for improving the proxy-based Web caching system using Web mining. It integrates Web caching and Pre-fetching through an efficient clustering based pre-fetching technique.

ART2 Clustering of Multiple Web Objects for Qualitative Web Prefetching

ABSTRACTThe web resources in the World Wide Web are rising, to large extent due to the services and applications provided by it. Because web traffic is large, gaining access to these resources incurs user-perceived latency. Although the latency can never be avoided, it can be minimized to a larger extent. Web prefetching is identified as a technique that anticipates the user’s future requests and fetches them into the cache prior to an explicit request made. Because web objects are of various types, a new algorithm is proposed that concentrates on prefetching embedded objects, including audio and video files. Further, clustering is employed using adaptive resonance theory (ART)2 in order to prefetch embedded objects as clusters. For comparative study, the web objects are clustered using ART2, ART1, and other statistical techniques. The clustering results confirm the supremacy of ART2 and, thereby, prefetching web objects in clusters is observed to produce a high hit rate.

Study of Distance Measurement Techniques in Context to Prediction Model of Web Caching and Web Prefetching

Internet is the boon in modern era as every organization uses it for dissemination of information and ecommerce related applications. Sometimes people of organization feel delay while accessing internet in spite of proper bandwidth. Prediction model of web caching and prefetching is an ideal solution of this delay problem. Prediction model analysing history of internet user from server raw log files and determine future sequence of web objects and placed all web objects to nearer to the user so access latency could be reduced to some extent and problem of delay is to be solved. To determine sequence of future web objects, it is necessary to determine proximity of one web object with other by identifying proper distance metric technique related to web caching and prefetching. This paper studies different distance metric techniques and concludes that bio informatics based distance metric techniques are ideal in context to Web Caching and Web Prefetching.

Improved Performance by Combining Web Pre-Fetching Using Clustering with Web Caching Based on SVM Learning Method

Combining Web caching and Web pre-fetching results in improving the bandwidth utilization, reducing the load on the origin server and reducing the delay incurred in accessing information. Web pre-fetching is the process of fetching the Web objects from the origin server which has more likelihood of being used in future. The fetched contents are stored in the cache. Web caching is the process of storing the popular objects ”closer” to the user so that they can be retrieved faster. In the literature many interesting works have been carried out separately for Web caching and Web pre-fetching. In this work, clustering technique is used for pre-fetching and SVM-LRU technique forWeb caching and the performance is measured in terms of Hit Ratio (HR) and Byte Hit Ratio (BHR). With the help of real data, it is demonstrated that the above approach is superior to the method of combining clustering based prefetching technique with traditional LRU page replacement method for Web caching.

Analysis and Design of Prefetching Framework for Mozilla Firefox

The presence of number of web sites has increased the user's attraction towards web objects. This tremendous use came up with the future requests prediction depending upon the current and past access behaviour. Use of Internet has boomed up a lot since the last decade. This use also came with the heavy load on the internet. In today's world, speed plays a significant role and hence the speed augmentation is one of the biggest issues. For this, web latency reduction by prefetching is one of the good ideas. For the same, web prefetching is performed, where user's next expected requests are prefetched in the web cache of the web browser. A browser is basically a GUI based application program, which provides a platform for running Internet. Mozilla Firefox is a web browser, which is in very much use these days. This paper provides an analysis of Mozilla Firefox prefetching technique (link and DNS prefetching) and then designs a new prefetching scheme for the same. The experimental results are performed in Matlab 5.0. The results show that the designed prefetching framework is more efficient in terms of the cache hit ratio. Index Terms—Mozilla Firefox, Prefetching, Cache, Browser, Link, DNS.

Combining Pre-fetching and Intelligent Caching Technique (SVM) to Predict Attractive Tourist Places

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.

Pre-eminence of Combined Web Pre-fetching and Web Caching Based on Machine Learning Technique

It is always wise to keep the frequently used information by various users in the cache memory. This makes the users feel that their needed information is available almost immediately. Apart from experiencing less access delay, caching process also helps in better bandwidth utilization and load reduction in the origin server. Pre-fetching is the process of fetching few of the Web pages in advance by assuming that those pages will be needed by the user in the near future. Combining pre-fetching and caching techniques results in experiencing much less access delay and much better bandwidth utilization. Many works have been reported in the literature, separately for Web caching techniques and pre-fetching of Web pages. In this paper, pre-fetching technique that uses clustering is combined with SVM (support vector machine)–LRU algorithm, a machine learning method for Web proxy caching. By using real-time data, it is demonstrated that the latter approach will be advantageous than clustering-based pre-fetching technique using traditional LRU-based caching policy. The efficiency of our proposed method is also compared with caching using Bayesian networks and neuro-fuzzy system.