Sequantial data are important in many real world location based services. In this paper, we study the problem of sequence matching. Specifically, we want to identify the sequences most similar to a given sequence, under three most commonly used preferece-aware similarity measures, i.e., Fagin’s intersection metric, Kendall’s tau, and Spearman’s footrule. We first analyze the properties of these three preference-aware similarity measures, revealing the connection between them and set intersection. Then, we build an index structure, which is essentially a doubly linked list, to facilitate efficient sequence matching. Lower- and upper-bounds are derived to achieve support prefix-based filtering. Experiments on various datasets show that our proposed method outperforms the baselines by a large margin.

中文翻译：

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基于位置的服务的优先级感知序列匹配

在许多现实世界中，基于位置的服务中重要的数据都是重要的。在本文中，我们研究了序列匹配问题。具体而言，我们要根据三种最常用的偏好感知相似性度量来识别与给定序列最相似的序列，即Fagin的相交度量，Kendall的tau和Spearman的尺规。我们首先分析这三种偏好感知相似性度量的属性，揭示它们与集合交集之间的联系。然后，我们建立一个索引结构，该结构本质上是一个双链表，以促进有效的序列匹配。导出上下限以实现支持基于前缀的过滤。在各种数据集上进行的实验表明，我们提出的方法在很大程度上优于基线。