Referrer Graph: A cost-effective algorithm and pruning method for predicting web accesses

Abstract

This paper presents the Referrer Graph (RG) web prediction algorithm and a pruning method for the associated graph as a low-cost solution to predict next web users accesses. RG is aimed at being used in a real web system with prefetching capabilities without degrading its performance. The algorithm learns from users accesses and builds a Markov model. These kinds of algorithms use the sequence of the user accesses to make predictions. Unlike previous Markov model based proposals, the RG algorithm di↵erentiates dependencies in objects of the same page from objects of di↵erent pages by using the object URI and the referrer in each request. Although its design permits us to build a simple data structure that is easier to handle and, consequently, needs lower computational cost in comparison with other algorithms, a pruning mechanism has been devised to avoid the continuous growing of this data structure. Results show that, compared with the best prediction algorithms proposed in the open literature, the RG algorithm achieves similar precision values and page latency savings but requiring much less computational and memory resources. Furthermore, when pruning is applied, additional and notable resource consumption savings can be achieved without degrading original performance. In order to reduce further the resource consumption, a mechanism to prune de graph has been devised, which reduces resource consumption of the baseline system without degrading the latency savings.

Pengujian Aplikasi Web - Tinjauan Pustaka Sistematis

“…Peneliti membagi skema pengujian dari sisi user dengan algoritma K-Means dan menggunakan model Markov untuk melakukan estimasi reliability dari aplikasi web. Pengujian beban kerja hasil dari perbaikan terhadap teknik yang telah ada juga telah dilakukan dalam artikel penelitian [10], [13], [14], [19], [20], dan [26]. Penelitian Proposal Solusi dengan melakukan optimasi xpath locator yang diujikan dalam enam aplikasi web dipaparkan oleh artikel penelitian [21] dan [23].…”

“…Hal ini sesuai dengan analisis bahwa penelitian untuk meningkatkan kecepatan, kestabilan, kemampuan dalam skalabilitas, maupun reliabilitas dalam beban berat menjadi tantangan yang banyak diteliti untuk mendapatkan solusi. Teknik-teknik dalam pengujian juga dikembangan misalnya dengan melibatkan algoritma prediksi yang dipaparkan oleh peneliti [14] untuk web prefetching dalam mengurangi web latency. Pengujian beban kerja secara dinamis juga lebih ditekankan dalam penelitian [10], [13], dan [19] dengan membandingkan pengujian secara statis.…”

Imbalanced Data Sample Pruning Algorithm Based on Dynamic threshold K Nearest Neighbor

“…Therefore, we usually need to prune the samples in the application of these data sets and pruning algorithm has become an important research hot-spot in recent years. Such as, Dai proposed an ensemble pruning algorithm based on randomized greedy selective strategy and ballot [1] ; Spanish researcher put forward a cost-effective pruning method for predicting web accesses [2] ; Vukovic present a sequential learning pruning algorithm of hyper basis function neural network for function approximation [3] . Pruning algorithm is also widely used in the fields of biology [4] , economy [5] and medicine [6] etc.…”