Research Issues of Outlier Detection in Trajectory Streams Using GPUs

Abstract

The widespread availability of sensors like GPS and traffic cameras has made it possible to collect large amounts of spatio-temporal data. One such type of data are trajectories, each of which consists of a time-ordered sequence of positions that a moving object occupies in space as time goes by. Trajectories can be streamed in real time from sensors, and because of this, they capture the current state of moving objects. For this reason, trajectories can be used in applications such as the real-time detection of senior citizens who have just fallen or who have just gotten lost outdoors, the real-time detection of drunk drivers, and the real-time detection of enemy forces in the battlefield. These applications involve the identification of trajectories with anomalous behaviors, and require fast processing in order to take immediate preventive action. However, outlier detection poses challenges stemming from both the complexity of the data and of the task. One way to address this is through parallel architectures like GPUs. In this paper, we present the problem of outlier detection in trajectory streams, and discuss the research issues that should be addressed by new outlier detection techniques for trajectory streams on GPUs.