Nowadays, various sensors are collecting, storing and transmitting tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network band and computing resource. Line simplification (LS) algorithms are an effective approach to attacking this issue by compressing data points in a trajectory to a set of continuous line segments, and are commonly used in practice. However, existing LS algorithms are not sufficient for the needs of sensors in mobile devices. In this study, we first develop a one-pass error bounded trajectory simplification algorithm (OPERB), which scans each data point in a trajectory once and only once. We then propose an aggressive one-pass error bounded trajectory simplification algorithm (OPERB-A), which allows interpolating new data points into a trajectory under certain conditions. Finally, we experimentally verify that our approaches (OPERB and OPERB-A) are both efficient and effective, using four real-life trajectory datasets.
@article{arxiv.1702.05597,
title = {One-Pass Error Bounded Trajectory Simplification},
author = {Xuelian Lin and Shuai Ma and Han Zhang and Tianyu Wo and Jinpeng Huai},
journal= {arXiv preprint arXiv:1702.05597},
year = {2017}
}
Comments
published at the 43rd International Conference on Very Large Data Bases (VLDB), Munich, Germany, 2017