Mining swarms from moving object data streams

Abstract

Current methods for mining groups from moving object data work with entire data stream. However, there are several emerging applications such as traffic management and urban emergency response systems which often need to identify groups from recent window of the moving object data. These requirements mandate algorithmic solutions that are time and memory efficient for adding new data incrementally and removing stale data. In this paper, we consider the problem of finding closed swarms over a sliding window. Large search space for computing closed swarms from the new data is the main key challenge in computing closed swarms over a sliding window. None of the existing methods are efficient for this. This paper presents an efficient incremental graph-based method for computing swarms over sliding windows. We demonstrate the performance of our method on two real datasets. The results show that our method is significantly faster than the existing incremental method over sliding windows with small overhead in memory. In particular, our method is shown to be 5-13 times faster with 2-6 times memory overhead in all the experiments.