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Discovery of closed spatio-temporal sequential patterns from event data

机译：从事件数据中发现闭合的时空顺序模式

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In the paper, we first thoroughly examine and prove properties of the participation index of spatio-temporal sequential patterns. Then, we introduce notions of a closure of a spatio-temporal sequential pattern and a closed spatio-temporal sequential pattern, as well as investigate and prove their properties. In particular, we prove that the set of all participation index strong closed spatio-temporal sequential patterns constitues a lossless representation of all participation index strong spatio-temporal sequential patterns. We also propose an algorithm, called CST-SPMiner, for discovering all participation index strong closed spatio-temporal sequential patterns. CST-SPMiner is an adaptation of the STBFM algorithm, which was proposed recently for the discovery of spatio-temporal sequential patterns with high participation index. While STBFM uses the CSP-tree structure for time-efficient candidate patterns generation and evaluation, CST-SPMiner uses it also for fast identification of closed patterns. Efficiency and effectiveness of our algorithm were verified on real crime data for Boston.
机译：在本文中，我们首先彻底检查并证明时空顺序模式参与指数的性质。然后，我们介绍了时空连续模式的闭合和时空连续模式的闭合的概念，并研究和证明了它们的性质。特别地，我们证明了所有参与指数强时空封闭序列模式的集合构成了所有参与指数强时空连续序列模式的无损表示。我们还提出了一种称为CST-SPMiner的算法，用于发现所有参与索引强封闭的时空时序模式。 CST-SPMiner是STBFM算法的改编，最近被提出用于发现具有高参与指数的时空顺序模式。虽然STBFM使用CSP树结构进行时间高效的候选模式生成和评估，但CST-SPMiner还将其用于快速识别闭合模式。在波士顿的真实犯罪数据上验证了我们算法的效率和有效性。

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