Preference Utility algorithm using GPGPU architecture

Nowadays, with the explosive growth of the network technologies many new applications and services have been developed on Internet. World Wide Web can provide these services provided without the limitation of time and location. Obviously, the number of user is dramatically increasing from amount of the visitations of web pages. In our previous work, we proposed an algorithm to discover more significant information from visited web pages to provide this information to web designers or policy makers to adjust the presentation of their Web contents. However, this algorithm is time-consuming approach due to it needs to scan the whole database many times. Therefore, we propose a GPGPU-based Preference Utility algorithm to enhance the performance by GPGPU parallel model. The proposed algorithm is developed on NVIDIA CUDA architecture. The experimental results show that the proposed method can achieve about 7x times over CPU-based method. The proposed algorithm can used to mine the information from web log data efficiently.