论文标题
高效有效的类似的子区域搜索，并深入强化学习
Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning
论文作者
论文摘要
类似的轨迹搜索是一个基本问题，在过去的二十年中已经进行了很好的研究。但是，类似的子三号搜索（SIMSUB）问题，目的是返回与查询轨迹最相似的轨迹的一部分（即，子三角洲）的问题，尽管它可能会忽略，尽管它大多可以忽略它，尽管它可以以较大的方式捕获轨迹相似性，并且许多应用程序以基本的分析为基础分析，以较少的应用程序以较高的应用程序为基础。在本文中，我们研究了SIMSUB问题，并开发了包括精确和近似算法的一组算法。在这些近似算法中，基于深度强化学习的两个算法脱颖而出，在有效性和效率方面胜过那些基于非学习的算法。我们对现实世界轨迹数据集进行了实验，这些实验验证了所提出的算法的有效性和效率。
Similar trajectory search is a fundamental problem and has been well studied over the past two decades. However, the similar subtrajectory search (SimSub) problem, aiming to return a portion of a trajectory (i.e., a subtrajectory) which is the most similar to a query trajectory, has been mostly disregarded despite that it could capture trajectory similarity in a finer-grained way and many applications take subtrajectories as basic units for analysis. In this paper, we study the SimSub problem and develop a suite of algorithms including both exact and approximate ones. Among those approximate algorithms, two that are based on deep reinforcement learning stand out and outperform those non-learning based algorithms in terms of effectiveness and efficiency. We conduct experiments on real-world trajectory datasets, which verify the effectiveness and efficiency of the proposed algorithms.
