Spatio-Temporal Contact Mining for Multiple Trajectories-of-Interest

Abstract

Spatio-temporal trajectory is a movement of an object in space over a certain time period, represented by a series of nodes composed of geospatial location and corresponding timestamp. A large amount of spatio-temporal trajectory data is being gathered through various trajectory acquiring devices by tracking the movement of objects such as people, animals, vehicles and natural events. Various trajectory data mining techniques have been proposed to discover useful patterns to understand the behaviour of spatio-temporal trajectories. One unexplored pattern is to identify potential contacts of targeted trajectories which can be defined as contact mining, that is useful for many applications. One such example would be to identify potential victims from known infected humans or animals, especially when the victims are asymptomatic in a rapid spread of infectious disease environments. Another one would be to identify individuals who have been close contacts with known terrorist networks or law breakers. This paper proposes a robust contact mining framework to efficiently and effectively mine contacts of multiple trajectories-of-interest from a given set of spatio-temporal trajectories. Experimental results demonstrate the efficiency, effectiveness and scalability of our approach. In addition, parameter sensitivity analysis reveals the robustness and insensitivity of our framework.

Keywords

• Contact mining
• data mining
• multiple trajectories-of-interest
• spatio-temporal trajectories