Investigation and performance improvement of web cache recommender system

A number of large and small scale applications are developed now in these days for fulfilling the users need. In recent years the Web based applications are also growing rapidly. Due to this the network performance is affected and browsing experience becomes slow. Thus performance improvement of traditional browsing and prefetching techniques are required, by which the application speed is optimized and delivers the high performance Web pages. Thus, in this paper pre-fetching techniques are investigated, and for cache replacement a recommendation system is developed. In order to design recommendation engine a promising data model is find in [6]. The given system utilizes the proxy access log for data analysis. The main advantage of proxy access log, it contains entire navigations of Web pages by a targeted user. This data model offers high performance outcomes. But computational complexity is not much adoptable. Thus the traditional data model is modified using a new scheme, where the K-mean algorithm is applied for user data personalization. Then after ID3 algorithm is used, for learning the user navigation patterns and KNN and probability theory is utilized for predicting the upcoming Web URLs for pre-fetching. The proposed data model is implemented using visual studio framework and the performance of the system are evaluated and compared in terms of memory used, time consumption, accuracy and error rate. According to the obtained results the proposed predictive system offers high performance results as compared to the traditional data model.