Geolocated time series, i.e., time series associated with certain locations, abound in many modern applications. In this paper, we consider hybrid queries for retrieving geolocated time series based on filters that combine spatial distance and time series similarity. For the latter, unlike existing work, we allow filtering based on local similarity, which is computed based on subsequences rather than the entire length of each series, thus allowing the discovery of more fine-grained trends and patterns. To efficiently support such queries, we first leverage the state-of-the-art BTSR-tree index, which utilizes bounds over both the locations and the shapes of time series to prune the search space. Moreover, we propose optimizations that check at specific timestamps to identify candidate time series that may exceed the required local similarity threshold. To further increase pruning power, we introduce the SBTSR-tree index, an extension to BTSR-tree, which additionally segments the time series temporally, allowing the construction of tighter bounds. Our experimental results on several real-world datasets demonstrate that SBTSR-tree can provide answers much faster for all examined query types. This paper has been published in the 27th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019).

中文翻译：

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使用混合索引的地理位置时间序列上的局部相似性搜索

地理定位的时间序列，即与某些位置相关的时间序列，在许多现代应用中比比皆是。在本文中，我们考虑基于混合空间距离和时间序列相似性的过滤器检索地理位置时间序列的混合查询。对于后者，与现有工作不同，我们允许基于局部相似性进行过滤，该相似性是基于子序列而不是每个序列的整个长度进行计算的，因此可以发现更细粒度的趋势和模式。为了有效地支持此类查询，我们首先利用最新的BTSR-tree索引，该索引利用位置和时间序列形状的界限来修剪搜索空间。而且，我们提出了优化方案，这些优化方案会在特定的时间戳下进行检查，以识别可能超出所需的局部相似性阈值的候选时间序列。为了进一步提高修剪能力，我们引入了SBTSR-tree索引，它是BTSR-tree的扩展，它另外在时间上划分了时间序列，从而可以构建更紧密的边界。我们在几个实际数据集上的实验结果表明，SBTSR-tree可以为所有检查的查询类型提供更快的答案。该论文已发表在第27届国际地理信息系统进步国际会议（ACM SIGSPATIAL 2019）上。我们在几个实际数据集上的实验结果表明，SBTSR-tree可以为所有检查的查询类型提供更快的答案。该论文已发表在第27届国际地理信息系统进步国际会议（ACM SIGSPATIAL 2019）上。我们在几个实际数据集上的实验结果表明，SBTSR-tree可以为所有检查的查询类型提供更快的答案。该论文已发表在第27届国际地理信息系统进步国际会议（ACM SIGSPATIAL 2019）上。