In this work, we study the performance of state-of-the-art access methods to efficiently store and retrieve trajectories in spatial networks. First, we study how efficiently such methods can manage trajectory data to support indexing for data demanding applications where trajectory retrieval must be fast. At the same time, trajectory insertions, deletions and modifications should also be executed efficiently. Secondly, we compare the performance of progressive processing of trajectory similarity top-k queries, which is a common query in spatial applications. Specifically, we examine FNR-trees (Frentzos 2003) and MON-trees (de Almeida and Gueting, 2005), which have been proposed for trajectory management, against a novel variation of our proposed Cluster-extended Adjacency Lists (CeAL) (Tiakas and Rafailidis 2015). In particular: (a) we extend the above access methods to efficiently handle trajectories of objects that move in large spatial networks, and (b) to enhance their performance, we create an entirely new implementation framework to generate trajectories and to test the trajectory management and retrieval for each approach. With respect to the generation of trajectories, we extend the generator by Brinkhoff (2000) to efficiently support very large spatial networks. Finally, we conduct extensive experimentation which demonstrates that the proposed method CeAL prevails in space and time complexity.

在这项工作中，我们研究了在空间网络中有效存储和检索轨迹的最新访问方法的性能。首先，我们研究了这种方法如何有效地管理轨迹数据，以支持轨迹检索必须快速的数据需求应用程序的索引。同时，轨迹的插入，删除和修改也应有效执行。其次，我们比较轨迹相似性top- k的渐进处理性能查询，这是空间应用程序中的常见查询。具体来说，针对我们提议的集群扩展邻接列表（CeAL）（Tiakas和1991）的新颖变体，我们研究了已提出用于轨迹管理的FNR树（Frentzos 2003）和MON树（de Almeida and Gueting，2005）。 Rafailidis 2015）。特别是：（a）我们扩展了上述访问方法以有效地处理在大型空间网络中移动的对象的轨迹，并且（b）为了增强其性能，我们创建了一个全新的实现框架来生成轨迹并测试轨迹管理并针对每种方法进行检索。关于轨迹的生成，我们通过Brinkhoff（2000）扩展了生成器，以有效地支持非常大的空间网络。最后，