Parallelized user clicks recognition from massive HTTP data based on dependency graph model
Cheng Fang, Jun Liu, Zhenming Lei · China Communications · 2014
With increasingly complex website structure and continuously advancing web technologies, accurate user clicks recognition from massive HTTP data, which is critical for web usage mining, becomes more difficult. In this paper, we propose a dependency graph model to describe the relationships between web requests. Based on this model, we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology. We evaluate the proposed algorithm with real massive data. The size of the dataset collected from a mobile core network is 228.7GB. It covers more than three million users. The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods.