Swarm: Mining Relaxed Temporal Moving Object Clusters

Recent improvements in positioning technology make massive moving object data widely available. One important analysis is to filed the moving objects that travel together. Existing methods put a strong constraint in defining moving object cluster, that they require the moving objects to stick together for consecutive timestamps. Our key observation is that the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps. Motivated by this, we propose the concept of swarm which captures the moving objects that move within arbitrary shape of clusters for certain timestamps that are possibly nonconsecutive. The goal of our paper is to rind all discriminative swarms, namely closed swarm. While the search space for closed swarms is prohibitively huge, we design a method, ObjectGrowth, to efficiently retrieve the answer. In ObjectGrowth, two effective pruning strategies are proposed to greatly reduce the search space and a novel closure checking rule is developed to report closed swarms on-the fly. Empirical studies on the real data as well as large synthetic data demonstrate the effectiveness and efficiency of our methods.