Discovering abnormal civil aviation requirements by analyzing users' online query behaviors

Abstract

Changes of users' query volume in online fight ticketing systems indicate the changes of requirements in civil aviation markets. By analyzing the big data of users' online query behaviors, we can timely and accurately discover abnormal civil aviation requirements. This ability is very conducive for airlines and agencies for taking immediate and effective marketing actions. In this paper, we propose a novel method to discover abnormal civil aviation requirements based on time-series curves of users' query volumes. In addition, we utilize the domestic airline route network to optimize the anomaly detection results from the perspective of a global network rather than that of a single airline. We conduct experiments on real-world users' query datasets collected from an online ticketing site. The experimental results demonstrate that the proposed method can effectively discover abnormal civil aviation requirements from users' online query logs.