An Efficient Online Approach for Direction-Preserving Trajectory Simplification with Interval Bounds
Bingqing Ke, Jie Shao, Dongxiang Zhang · 2017
The prevalence of GPS devices has facilitated collection of large-scale trajectories. Fresh positions of moving objects can be sampled periodically and sent to servers for data analytics and query processing. Online trajectory simplification is a compression task usually conducted at the sensor side and serves as a key component to reduce network communication overhead. In this paper, we study a new trajectory simplification problem which is direction-preserving and works in an online fashion. An efficient simplification algorithm is proposed, which is guaranteed to be error-bounded and achieves O(n) time and O(1) space complexity. In an extensive experimental evaluation with two real datasets, our approach exhibits superior performance on both running time and compression rate.