Various mobile devices have been used to collect, store and transmit tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network bandwidth and computing resource. To attack this issue, one-pass line simplification (\(\textsf {LS} \)) algorithms have been developed, by compressing data points in a trajectory to a set of continuous line segments. However, these algorithms adopt the perpendicular Euclidean distance, and none of them uses the synchronous Euclidean distance (\(\textsf {SED} \)), and cannot support spatiotemporal queries. To do this, we develop two one-pass error bounded trajectory simplification algorithms (\(\textsf {CISED} \)-\(\textsf {S} \) and \(\textsf {CISED} \)-\(\textsf {W} \)) using \(\textsf {SED} \), based on a novel spatiotemporal cone intersection technique. Using four real-life trajectory datasets, we experimentally show that our approaches are both efficient and effective. In terms of running time, algorithms \(\textsf {CISED} \)-\(\textsf {S} \) and \(\textsf {CISED} \)-\(\textsf {W} \) are on average 3 times faster than \(\textsf {SQUISH} \)-\(\textsf {E} \) (the fastest existing \(\textsf {LS} \) algorithm using \(\textsf {SED} \)). In terms of compression ratios, \(\textsf {CISED} \)-\(\textsf {S} \) is close to and \(\textsf {CISED} \)-\(\textsf {W} \) is on average \(19.6\%\) better than \(\textsf {DPSED} \) (the existing sub-optimal \(\textsf {LS} \) algorithm using \(\textsf {SED} \) and having the best compression ratios), and they are \(21.1\%\) and \(42.4\%\) better than \(\textsf {SQUISH} \)-\(\textsf {E} \) on average, respectively.

中文翻译：

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使用同步欧几里德距离简化一遍轨迹

已经使用各种移动设备来收集，存储和传输巨大的轨迹数据，并且众所周知，原始轨迹数据严重浪费了存储，网络带宽和计算资源。为了解决这个问题，通过将轨迹中的数据点压缩为一组连续的线段，开发了单程线简化（\（\ textsf {LS} \））算法。但是，这些算法采用垂直欧几里德距离，并且都没有使用同步欧几里德距离（\（\ textsf {SED} \）），并且不支持时空查询。为此，我们开发了两种单次通过误差边界的轨迹简化算法（\（\ textsf {CISED} \） -\（\ textsf {S} \）和\（\ textsf {CISED} \） - \（\ textsf {W} \））使用\（\ textsf {SED} \），基于一种新颖的时空锥相交技术。通过使用四个真实的轨迹数据集，我们实验证明了我们的方法既有效又有效。就运行时间而言，算法\（\ textsf {CISED} \） - \（\ textsf {S} \）和\（\ textsf {CISED} \） - \（\ textsf {W} \）平均为3比\（\ textsf {SQUISH} \） - \（\ textsf {E} \）快1倍（使用\（\ textsf {SED} \]的现有最快\\\ textsf {LS} \）算法）。就压缩率而言，\（\ textsf {CISED} \） - \（\ textsf {S} \）接近，而\（\ textsf {CISED} \） - \（\ textsf {W} \）处于打开状态平均\（19.6 \％\）优于\（\ textsf {DPSED} \）（使用\（\ textsf {SED} \）并具有最佳压缩率的现有次优\（\ textsf {LS} \）算法比率），它们分别比\（\ textsf {SQUISH} \） - \（\ textsf {E} \）分别高\（21.1 \％\）和\（42.4 \％\）。