Web Prefetching Techniques Research Articles

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%.

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.

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

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.

Novel Approaches for Integrating MART1 Clustering Based Pre-Fetching Technique with Web Caching

Web caching and Web pre-fetching are two important techniques for improving the performance of Web based information retrieval system. These two techniques would complement each other, since Web caching provides temporal locality whereas Web pre-fetching provides spatial locality of Web objects. However, if the web caching and pre-fetching are integrated inefficiently, this might cause increasing the network traffic as well as the Web server load. Conventional policies are most suitable only for memory caching since it involves fixed page size. But when one deals with web caching which involves pages of different size. Hence one need an efficient algorithm that works better in web cache environment. Moreover conventional replacement policies are not suitable in clustering based pre-fetching environment since multiple objects were pre-fetched. Hence, it cannot be handled by conventional algorithms. Therefore, care must be taken while integrating web caching with web pre-fetching technique in order to overcome these limitations. In this paper, novel algorithms have been proposed for integrating web caching with clustering based pre-fetching technique. Here Modified ART1 has been used for clustering based pre-fetching technique. The proposed algorithm outperforms the traditional algorithms in terms of hit rate and number of objects to be pre-fetched. Hence saves bandwidth.

Single-client, multi-servers web pre-fetching technique

Single-client, multi-servers web pre-fetching technique