论文标题
限制：一种有效的轨迹简化方法
LiMITS: An Effective Approach for Trajectory Simplification
论文作者
论文摘要
轨迹代表移动对象的移动性，因此在数据挖掘应用程序中具有巨大价值。但是，轨迹数据的量很大，因此直接存储和处理原始数据是昂贵的。轨迹也是冗余的，因此可以应用数据压缩技术。在本文中，我们提出了有效的算法来简化轨迹。我们首先将现有算法替换为$ l_ \ infty $ metric的常用$ L_2 $公制，以便将它们推广到高维空间（例如，实际上是3个空间）。接下来，我们提出了一种新颖的方法，即L侵入性多维插值轨迹简化（限制）。限制属于弱简化，并利用$ l_ \ infty $公制。它通过多维插值生成简化的轨迹。它还允许一种称为紧凑型表示的新格式，以进一步提高压缩比。最后，我们通过在现实世界数据集上的实验来证明限制的性能，这表明它比其他现有方法更有效。
Trajectories represent the mobility of moving objects and thus is of great value in data mining applications. However, trajectory data is enormous in volume, so it is expensive to store and process the raw data directly. Trajectories are also redundant so data compression techniques can be applied. In this paper, we propose effective algorithms to simplify trajectories. We first extend existing algorithms by replacing the commonly used $L_2$ metric with the $L_\infty$ metric so that they can be generalized to high dimensional space (e.g., 3-space in practice). Next, we propose a novel approach, namely L-infinity Multidimensional Interpolation Trajectory Simplification (LiMITS). LiMITS belongs to weak simplification and takes advantage of the $L_\infty$ metric. It generates simplified trajectories by multidimensional interpolation. It also allows a new format called compact representation to further improve the compression ratio. Finally, We demonstrate the performance of LiMITS through experiments on real-world datasets, which show that it is more effective than other existing methods.
