CiNCT：通过相对运动标记压缩和检索大规模车辆轨迹
CiNCT: Compression and retrieval for massive vehicular trajectories via relative movement labeling
摘要：
在本文中，我们提出了一个压缩的数据结构的道路网络中的移动对象的轨迹，这是表示为道路边缘序列。与现有的压缩方法在网络中的轨迹，我们的方法支持模式匹配和解压缩从任意位置，同时保持了高的压缩性与理论保证。具体来说，我们的方法是基于FM索引，快速和紧凑的数据结构的模式匹配。为了增强压缩，我们将道路网络的稀疏性纳入到数据结构中。特别是，我们提出了新的概念，相对运动标记和伪秩，每个有助于显着减少数据大小和查询处理时间。我们的理论分析和实验研究表明，我们提出的方法相比，现有的轨迹压缩方法和FM指数的变种的优势。
英文摘要：
In this paper, we present a compressed data structure for moving object trajectories in a road network, which are represented as sequences of road edges. Unlike existing compression methods for trajectories in a network, our method supports pattern matching and decompression from an arbitrary position while retaining a high compressibility with theoretical guarantees. Specifically, our method is based on FM-index, a fast and compact data structure for pattern matching. To enhance the compression, we incorporate the sparsity of road networks into the data structure. In particular, we present the novel concepts of relative movement labeling and PseudoRank, each contributing to significant reductions in data size and query processing time. Our theoretical analysis and experimental studies reveal the advantages of our proposed method as compared to existing trajectory compression methods and FM-index variants.
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